University of Southern California Dissertations and Theses

Addicted to androgens: consequences for cognition and behavior

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Addicted to androgens: consequences for cognition and behavior

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Content Copyright 2020 Lisa Beth Dokovna

ADDICTED TO ANDROGENS:
CONSEQUENCES FOR COGNITION AND BEHAVIOR

by

Lisa Beth Dokovna

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)

May 2020

ii

“The world is a thing of utter inordinate complexity,
and richness and strangeness that is absolutely awesome.
I mean the idea that such complexity can arise not only out of such simplicity,
but probably absolutely out of nothing,
is the most fabulous extraordinary idea”

-Douglas Adams

Wish you Were Here: The Official Biography of Douglas Adams
By Nick Webb 2005

iii
Acknowledgements
First and foremost, I would like to thank my advisor Dr. Ruth Wood, for her assistance
and guidance throughout the years. I remember first meeting Ruth during recruitment
weekend, where she was interviewing me and said something along the lines of “You must
be wondering how someone who gives testosterone to rats wants to talk to you”. I never
realized just how serendipitous that meeting was, or could have imagined in the several
years after, that I would be so excited by research that gives testosterone to rats too. My
previous research experience working with neuroanatomy and behavior made her lab an
ideal environment to transition to after a harrowing experience in my former lab at USC.
I will be forever grateful for the opportunity to pursue my dissertation research in her lab,
to be trusted with her research questions, and use them as a springboard to generate many
of my own. Ruth’s mentorship has helped me grow into the scientist I am today.

I am grateful for the contributions and efforts of my dissertation committee, Dr. Scott
Kanoski for serving as chair for my defense and Dr. Alexandre Bonnin, who was part of
my committee from the very beginning. Additional thanks to Drs. Christian Pike and Alan
Watts for serving on my qualification committee. I especially want to thank Dr. John
Monterosso for his advisement on both committees, I have always greatly appreciated his
insight on our rodent models of decision-making and his thoughtful questions.

Next I would like to thank my lab mate and dearest friend, Alexandra Donovan. Not only
for her support with injections, behavioral testing, and data collection in Chapter 2, but
also for her advice and comments on early drafts of this manuscript. Without her un-
iv
wavering support, abundance of patience, and freakish talent with words, I would have
never been able to both complete this research and maintain my sanity. Thank you, from
the bottom of my heart. I look forward to the day when I get to call her doctor too. Sharing
an office with Alexandra and running animals in the basement together will be some of
the fondest memories of graduate school.

I would next like to thank all the present and former members of the Wood lab. Roy
Miller, for his enthusiasm and patience, letting me rehearse my defense several times over
zoom, as well his valuable input and suggestions. To Grace Li, our former lab technician
who assisted on running animals and data collection in Chapter 2. To my USC
undergraduate mentee Nika Moussavi, and Bridging-the-Gap mentees Jellisa Ewan and
Adora Moneme for their assistance with staining and data collection in Chapter 3. Special
appreciation and recognition should go to Sejla Hasecic; she was visiting the lab from
Sweden for a study abroad program and I had the pleasure of mentoring her thesis
research, which provided valuable contributions to Chapter 3. Special thanks to Dr. Katie
Wallin-Miller for her support and collaborative help early on when I first joined the lab.

Thank you to the USC Neuroscience Graduate Program and fellow graduate students. In
particular, special thanks to Deanna Solórzano, Program Manager and Dawn Burke,
Director of Student Services, for all their logistical assistance over the years. Thank you
to Dr. Kirsten Lynch, for her support and encouragement along the way.

I would like to thank my friends and family for their moral support and providing the
necessary reassurances needed complete my thesis. Special gratitude to my mother, who
v
has always supported me and encouraged my enthusiasm for learning. I especially wish
to thank my first research advisor, Dr. Mark Stanton. Working in his lab was the impetus
for my neuroscience research journey. I will be forever grateful for that first experience
that has led me down this path.

Lastly, I wish to express my gratitude for the rats and their valuable contributions to
science and the research in this doctorate: Lance, Armstrong, Barry, Bonds, Arnold,
Schwarz, Vlad, Dmitri, Sid and Nancy, Houdini, Gus Gus, and all the rest.

vi
Table of Contents
EPIGRAPH………………………………………………..……………………………………… ii
ACKNOWLEDGEMENTS ........................................................................... iii
LIST OF FIGURES ........................................................................................ x
ABSTRACT .................................................................................................. xi
CHAPTER 1. INTRODUCTION TO ANABOLIC ANDROGENIC STEROIDS AS
DRUGS OF ABUSE ....................................................................................... 1
1.1 OVERVIEW ..................................................................................................................... 1
1.2 BACKGROUND OF AAS ABUSE IN HUMANS ....................................................................... 1
1.3 RODENT MODELS OF AAS ABUSE .................................................................................. 10
1.4 ANDROGEN RECEPTORS IN THE BRAIN .......................................................................... 12
1.5 REWARDING AND REINFORCING PROPERTIES OF ANDROGENS ........................................ 13
1.6 CONCLUSION ............................................................................................................... 15
1.7 REFERENCES ............................................................................................................... 16
CHAPTER 2. ANABOLIC-ANDROGENIC STEROIDS AND COGNITIVE
EFFORT DISCOUNTING IN MALE RATS ................................................... 36
2.1 ABSTRACT ................................................................................................................... 36
2.2 INTRODUCTION ........................................................................................................... 37
2.3 MATERIALS AND METHODS ......................................................................................... 40
2.3.1 ANIMALS ............................................................................................................. 40
2.3.2 TESTOSTERONE TREATMENT ................................................................................. 41
vii
2.3.3 OPERANT CHAMBERS ............................................................................................ 41
2.3.4 TRAINING ............................................................................................................. 42
2.3.5 COGNITIVE EFFORT DISCOUNTING ......................................................................... 43
2.3.6 MONOAMINE DRUGS ............................................................................................. 44
2.3.7 DATA ANALYSIS ..................................................................................................... 45
2.4 RESULTS ..................................................................................................................... 47
2.4.1 EFFECT OF TESTOSTERONE AT BASELINE ................................................................. 47
2.4.2 D1R ANTAGONIST, SCH23390 ............................................................................. 49
2.4.3 D2R ANTAGONIST, ETICLOPRIDE ........................................................................... 50
2.4.4 SEROTONIN DEPLETION WITH PCPA ..................................................................... 52
2.5 DISCUSSION ................................................................................................................ 53
2.6 REFERENCES ............................................................................................................... 61
CHAPTER 3. ANDROGEN RECEPTOR-POSITIVE AFFERENTS TO THE
NUCLEUS ACCUMBENS IN MALE RATS .................................................. 68
3.1 ABSTRACT ................................................................................................................... 68
3.2 INTRODUCTION ........................................................................................................... 69
3.3 MATERIALS AND METHODS .......................................................................................... 71
3.3.1 ANIMALS .............................................................................................................. 71
3.2.2 TRACER INJECTION ............................................................................................... 71
3.2.3 PERFUSION AND SECTIONING ................................................................................ 72
3.2.4 CTB IMMUNOHISTOCHEMISTRY ............................................................................ 72
3.2.5 AR IMMUNOHISTOCHEMISTRY ............................................................................... 73
3.2.6 AR-CTB DOUBLE-LABELING ................................................................................. 73
viii
3.2.7 DATA ANALYSIS ..................................................................................................... 74
3.4 RESULTS ..................................................................................................................... 77
3.4.1 RETROGRADE TRACER INJECTIONS IN ACBC ........................................................... 77
3.4.2 RETROGRADE TRACER INJECTIONS IN ACBSH ......................................................... 79
3.4.3 TSA-ENHANCED IMMUNOREACTIVITY FOR AR ...................................................... 80
3.4.4 AR-CTB COLOCALIZATION .................................................................................... 81
3.5 DISCUSSION ................................................................................................................ 84
3.6 CONCLUSION .............................................................................................................. 89
3.7 REFERENCES .............................................................................................................. 90
CHAPTER 4. PAVLOVIAN CONDITIONED APPROACH AND ANABOLIC-
ANDROGENIC STEROIDS ......................................................................... 96
4.1 ABSTRACT ................................................................................................................... 96
4.2 INTRODUCTION ........................................................................................................... 97
4.3 MATERIALS AND METHODS .......................................................................................... 99
4.3.1 ANIMALS .............................................................................................................. 99
4.2.2 TESTOSTERONE TREATMENT ............................................................................... 100
4.3.3 OPERANT CHAMBERS .......................................................................................... 100
4.3.4 TRAINING AND TESTING ...................................................................................... 100
4.3.5 DATA ANALYSIS ................................................................................................... 101
4.4 RESULTS ................................................................................................................... 102
4.5 DISCUSSION .............................................................................................................. 108
4.6 REFERENCES ............................................................................................................. 113
ix
CHAPTER 5. DISCUSSION OF EXPERIMENTS AND BROADER
SIGNIFICANCE ........................................................................................ 122
5.1 SUMMARY OF FINDINGS ............................................................................................. 122
5.2 ANDROGENS, THE BRAIN AND BEHAVIOR .................................................................... 124
5.3 COMPLEXITIES OF AAS RECREATIONAL DRUG USE, ABUSE, AND ADDICTION ................. 127
5.4 CONCLUSION .............................................................................................................. 131
5.5 REFERENCES ............................................................................................................. 132

x
Table of Figures
Figure 1. Schematic of the operant chamber (A) and experimental design (B) for
cognitive effort discounting ................................................................................... 42
Figure 2. Behavioral responses on cognitive effort discounting .................................. 49
Figure 3. Effects of pharmacological dopaminergic manipulations on behavioral
outcomes for cognitive effort operant discounting .............................................. 51
Figure 4. Effect of serotonin depletion on behavioral outcomes for cognitive effort
operant discounting ............................................................................................... 53
Figure 5. Representative retrograde tracer injections in nucleus accumbens core
(AcbC) and shell (AcbSh), and a representative androgen receptor (AR)-
responsive afferent to AcbC core ........................................................................... 75
Figure 6. Visualization of androgen receptors (AR) is enhanced when combined with
tyramide signal amplification (TSA) .................................................................... 76
Figure 7. Androgen receptors (AR) are expressed in several brain regions that also
have direct input to the nucleus accumbens (Acb) ............................................... 78
Figure 8. Schematic representing the distribution of androgen receptor (AR)-
immunoreactive cells relative to CTB-labeled neurons. ....................................... 83
Figure 9. Recorded behavioral measures during stimulus presentation for Pavlovian
conditioned approach .......................................................................................... 104
Figure 10. Calculated behavioral measures for lever preference over food-cup during
stimulus presentation for Pavlovian conditioned approach ............................. 106
Figure 11. Average and individual PCA index scores and food-cup entries during non-
stimulus period for Pavlovian conditioned approach ........................................ 107
xi
Abstract
Anabolic-androgenic steroids (AAS) are drugs of abuse, taken to increase muscle mass
and athletic performance, but with negative effects on physiology and behavior. Similar
to other drugs of abuse, AAS users are at risk for developing maladaptive behaviors,
including impaired decision-making. Rats treated with high-dose testosterone, a model
of AAS abuse, show altered decision-making under physical effort. Since modern society
places greater emphasis on cognitive rather than physical effort, Chapter 2 evaluates the
effects of AAS on a novel operant discounting paradigm for decision-making under
cognitive effort. Cognitive effort operant discounting is not sensitive to high-dose
testosterone and does not require dopamine receptor activity. Because all AAS are
synthetic derivatives of testosterone, they can act via androgenic mechanisms in the
brain. Specifically, AAS can bind to the androgen receptor (AR) to influence behavior.
AAS affect dopamine-dependent behaviors mediated by the mesocorticolimbic pathway;
the nucleus accumbens (Acb) is the central hub in this pathway mediating reward and
decision-making. Chapter 3 utilizes improved immunostaining techniques to enhance
staining for AR in brain regions with low amounts of AR and identify AR-responsive
afferents to Acb. Several cognitive brain regions that project to Acb show robust AR
staining with tyramide signal amplification, but few Acb afferents are AR-responsive.
Presence of AR provides a potential mechanism by which AAS can influence cognitive
behaviors and decision-making. Although AAS users are at risk for developing
dependency, it remains unknown to what extent AAS foster drug-seeking behavior.
Chapter 4 determines whether high-dose testosterone alters reward learning dependent
on Acb DA to promote drug-seeking using a test of Pavlovian conditioned approach
xii
(PCA). Due to the high proportion of sign-tracking behavior regardless of treatment
condition, we were unable to make conclusions regarding the androgen sensitivity of PCA.
Taken together, these studies address the underlying neurobiology of AAS and how
androgens effect decision-making and drug-seeking behavior that contributes to
addiction.

1
Chapter 1. Introduction to anabolic androgenic steroids as drugs
of abuse

1.1 Overview
Public interest in anabolic-androgenic steroids (AAS) often peaks when the media reports
on doping scandals in competitive sports. Yet compared to other drugs of abuse, AAS
receive substantially less attention regarding the potential for harm and dependency. The
media often overlooks the potential negative physiological and psychological
consequences of AAS abuse. Perhaps as a result, little is known about the long-term
consequences of AAS use on brain and behavior. Nonetheless, it appears that many AAS
users experience adverse effects on cognitive function, and some go on to develop
substance abuse disorders. Rodent models of AAS abuse provide a valuable tool to
evaluate how androgens change behavior. In this introduction, I address what is
known about AAS and behavior, and highlight the gaps in our knowledge that the
following chapters attempt to address. Understanding the risks of AAS abuse is necessary
because illicit use continues to thrive in rank-and-file athletes seeking chemical short-
cuts to improve physical appearance, increase muscularity and strength.

1.2 Background of AAS abuse in humans
Steroids are organic compounds synthesized in the body from cholesterol. Steroid
hormones circulate throughout the body and serve as signaling molecules through
binding to steroid hormone receptors. Loosely categorized into two main types,
corticosteroids and gonadal steroids, steroid hormones encompass a broad range of
2
chemical messengers (Norman, 2003; Whirledge and Cidlowski, 2019). Progestogens,
estrogens, and androgens are the three principal types of gonadal steroids. Estrogens and
androgens contribute to the development and maintenance of female and male secondary
sex characteristics, respectively. In males, this includes increased muscle mass and bone
development mediated by the growth-promoting (anabolic) effects of androgens.
Testosterone is the primary androgen produced by the testes in males.

Testosterone was first isolated by Dutch and German chemists in 1935 (David et al., 1935;
Nieschlag and Nieschlag, 2019; Ruzicka and Wettstein, 1935). Soon after, synthetic
derivatives of testosterone, collectively known as AAS, became available for medical use
in the 1940s (Kanayama and Pope, 2018). Given the androgenic (masculinizing)
properties of AAS, doctors prescribe them for the treatment of male hypogonadism,
stimulation of the onset of puberty, and for hormone replacement therapy in older men.
In other situations, AAS are used for their potent anabolic effects to combat wasting
syndrome in cancer or AIDS patients by increasing muscle mass and preventing bone loss
(Brower, 2002; Kanayama and Pope, 2018). AAS work very effectively at building muscle
and reducing body fat. As a result, they are powerful drugs for enhancing athletic
performance (Kanayama et al., 2009b; Kanayama and Pope, 2012; Kanayama et al.,
2001). In the 1950s, AAS use became widespread among elite athletes as performance-
enhancing substances. First banned by the International Olympic committee in 1968,
testing for AAS increased in the 1970s (Alquraini and Auchus, 2018). Beginning in the
1980’s, anabolic steroid abuse spread beyond elite athletes. Classified as a controlled
substance in 1990, illicit use continues to grow, especially in the adolescent demographic
(Kanayama and Pope, 2018).
3

AAS abuse is widespread, with over 4 million users in the United States between the ages
of 13-50 years (Pope et al., 2014a). The global lifetime prevalence of AAS use is 3.3%; this
number is higher for males, 6.4%, than females, 1.6% (Sagoe et al., 2014b). High-profile
athletes and bodybuilders, like Lance Armstrong and Arnold Schwarzenegger, receive the
greatest attention for their use of AAS. Every Olympic year the media focusses their
attention on Olympians’ use of performance enhancing substances, with new reports of
doping scandals or state-sponsored schemes (Fitch, 2008, 2017; Fogel, 2017). This has
led to a stereotype in the public mind that AAS abuse is a problem of fairness in sport that
is restricted to a limited number of elite athletes. However, the majority of AAS users are
rank-and-file athletes and recreational body builders (Steele et al., 2019). Abuse of AAS
is most common for cosmetic purposes, with the intent of improving physical appearance
by increasing muscle mass and reducing body fat (Jampel et al., 2016; Miller et al., 2005;
Pope et al., 2012; Pope et al., 2017). Two studies of San Francisco gym members found
AAS abuse was highest among homosexual and bisexual males, between 10.0-21.6% (Ip
et al., 2019; Ip et al., 2017), who cite desire for increased muscle mass, strength, and
appearance as motivation for use (Ip et al., 2011; Sagoe et al., 2014a).

Over 60% of steroid users are under the age of 30, and 25% of users report starting
steroids during their teenage years (Pope et al., 2014a). The same risk factors for AAS
abuse in adults, i.e. eating disorders, body image problems, and sexual minorities, are
also true for adolescent males (Blashill et al., 2017; Hildebrandt et al., 2010; Jampel et al.,
2016; Pope et al., 2017). Conduct disorders, substance abuse, fighting and risky sexual
behavior are all contributing risk factors for adolescent AAS abuse (Miller et al., 2005;
4
Pope et al., 2012). Teenage AAS use is concerning, since the brain and body are still
developing, and AAS may potentiate preexisting psychological and behavioral issues
(Rohman, 2009).

The use of multiple different anabolic agents is common among AAS users who follow a
regimen of “stacking”, “cycling”, and “pyramiding” to maximize anabolic effects
(Grönbladh et al., 2016; Pope and Katz, 1994). Stacking refers to combining multiple AAS,
while cycling refers to a period of use followed by an abstinence period. Pyramiding is a
schedule of drug taking where the user increases and then decreases the dose to both
avoid plateauing and reduce withdrawal symptoms (Graham et al., 2008; Wood, 2008;
Wood and Stanton, 2012). AAS users may also include ‘accessory’ medications to combat
or limit unwanted side effects (Skårberg et al., 2009). Polypharmacy, the use of multiple
drugs, both prescription and illegal is common among AAS users (Dodge and Hoagland,
2011; Kanayama et al., 2009c; Kanayama and Pope, 2011, 2012). The type of AAS, route
of drug administration, and dosing schedule followed by users complicates our
understanding of the negative consequences of AAS on physiological and psychological
health.

Because AAS use is no longer restricted to a small community of elite professional
athletes, it is important to understand the extent of physiological, psychological, and
behavioral health risks from long-term AAS abuse. Viewed in this manner, AAS use is a
public health concern (Kanayama et al., 2008; Kanayama et al., 2018). Furthermore,
compared with research on other drugs of abuse, the relative lack of data on the negative
consequences of AAS abuse does not necessarily imply that AAS are benign.
5

The potential harm of AAS abuse on physiological health is well documented. AAS users
are predisposed to hematologic, cardiovascular (Baggish et al., 2017; Nieschlag and
Vorona, 2015), hepatic (Solimini et al., 2017), endocrine and reproductive dysfunction
(Casavant et al., 2007; Christou et al., 2017; Hartgens and Kuipers, 2004; Kanayama et
al., 2008; Maravelias et al., 2005; Pope et al., 2014b; van Amsterdam et al., 2010; Vorona
and Nieschlag, 2018). AAS increase red blood cell production and raises cholesterol (Pope
and Katz, 1994). These changes contribute to cardiovascular problems associated with
AAS, including hypertension, atherosclerosis, blood clot, and stroke (Baggish et al., 2017;
Baggish et al., 2010; Nieschlag and Vorona, 2015). In the liver, AAS abuse is associated
with decreased function, jaundice, and cancer (Solimini et al., 2017). The elevated levels
of circulating androgens achieved with AAS elevate risk of cancer in androgen-sensitive
tissues, especially the gonads (Salerno et al., 2018). Side effects of AAS abuse on the
reproductive system are extensive. These include changes in libido (Bahrke et al., 1992;
Hartgens and Kuipers, 2004), testicular atrophy and breast enlargement in males, and
clitoromegaly and menstrual irregularities in females. Most male users have persistently
low gonadotropin and testosterone levels that last for several weeks to months after AAS
withdrawal, often impairing fertility (Christou et al., 2017; Kanayama et al., 2015).

In addition to physiological consequences, AAS have adverse side effects on psychological
health. Anxiety and mood disorders can occur with AAS abuse (Bahrke et al., 1992;
Bahrke et al., 1996; Griffiths et al., 2018; Ip et al., 2012; Ip et al., 2015; Kanayama et al.,
2010b). 23% of users report symptoms of depression, mania, or hypomania (Pope and
Katz, 1988, 1994). Risk for depression and anxiety is especially high during withdrawal
6
(Ip et al., 2012; Oberlander and Henderson, 2012; Pope et al., 2010a; Pope et al., 2010b;
Uzych, 1992). However, psychological consequences of AAS use only affect a subset of
users. Differences in vulnerability or preexisting conditions confound our understanding
of the direct psychological impact of long-term AAS abuse (Kanayama et al., 2010a;
Mędraś et al., 2018; Piacentino et al., 2015; Porcerelli and Sandler, 1998).

Furthermore, some AAS users experience psychotic behavior. This includes the popular
image of a juiced-up, hulked-out user with steroid-induced rage, i.e. ‘roid rage. AAS users
can be impulsive, irritable, aggressive, and prone to acts of violence (Bahrke et al., 1992;
Hildebrandt et al., 2018; Kanayama et al., 2010b; Klötz et al., 2006; Midgley et al., 2001;
van Amsterdam et al., 2010). Controlled experiments giving an acute dose of AAS to males
without previous exposure find an increase in manic symptoms and aggression (Kouri et
al., 1995; Pope et al., 2000). There is a positive correlation between aggression and
testosterone levels (Oberlander and Henderson, 2012; Perry et al., 2003), and between
aggression and dose of AAS administered (Galligani et al., 1996; Pagonis et al., 2006;
Uzych, 1992). Yet, not all users experience steroid-induced rage (Dunn, 2015; Lundholm
et al., 2015). Individual temperament can contribute to experiences of impulsivity and
aggression under the influence of AAS (Midgley et al., 2001; Sagoe et al., 2016). Increased
responsivity to provocation may account for the increased aggression found in some users
(Midgley et al., 2001; Olweus et al., 1980, 1988). Essentially, AAS decrease the threshold
necessary to elicit an aggressive response, making users more reactive, impatient,
irritable, or “testy”.

7
Increased aggression in AAS users elevates risk for criminality (Klötz et al., 2006; Thiblin
and Pärlklo, 2002). AAS use is associated with violent crime (Conacher and Workman,
1989; Klötz et al., 2007) and behavior (Beaver et al., 2008), violence towards women
(Choi and Pope, 1994), and domestic violence (Schulte et al., 1993). Abuse of other drugs
does not confound the association between AAS use and violent crime (Klötz et al., 2007).
Similar to adult users, adolescent AAS users also participate in violent and high-risk
behaviors. These include carrying a weapon, unsafe sex, multiple drug use, and driving
after drinking (Middleman and DuRant, 1996; Middleman et al., 1995). Elevated
impulsivity and aggression combined with greater depressive symptoms increases risk of
suicide or violent death (Brower et al., 1989; Thiblin et al., 2000; Thiblin et al., 1999).

The focus of my thesis research is on changes in brain and behavior resulting from AAS
abuse. Emerging evidence finding impaired decision-making suggests negative outcomes
for cognition and executive function; these side effects have a detrimental impact on both
users and their communities (Kanayama et al., 2008; Kanayama et al., 2018). It becomes
increasingly necessary to address the gap in our knowledge regarding the effects of AAS
on the brain as the number of people affected by AAS abuse increases.

Until recently, the negative consequences of AAS on cognition and executive function
received little attention. Limited studies suggest cognitive impairments following both
acute and long-term abuse (Hildebrandt et al., 2014). AAS users have poor visuospatial
memory (Kanayama et al., 2013; Kaufman et al., 2015), diminished problem-solving
abilities (Bjørnebekk et al., 2019; Bond et al., 1995), greater distractibility, confusion, and
memory loss (Mhillaj et al., 2015; Su et al., 1993). Individuals with AAS dependence show
8
poor emotional recognition, especially of fearful stimuli (Hauger et al., 2019a).
Supraphysiological levels of androgens cause cell death (neurotoxicity) in vitro (Estrada
et al., 2006), and may contribute to the psychological and cognitive deficits found in vivo
(Kanayama et al., 2013). Potential neurotoxicity from chronic AAS use may increase risk
for dementia in aging and former users (Kanayama et al., 2018; Kaufman et al., 2015;
Kaufman et al., 2019). Human brain imaging studies identify both structural and
functional changes in AAS users consistent with brain changes in response to other drugs
of abuse (Moeller and Paulus, 2018). These include thinner cortex (Hauger et al., 2019b),
reduced cortical volume (Bjørnebekk et al., 2017), white matter abnormalities (Seitz et al.,
2017), and reduced functional connectivity between brain regions important for cognition
and emotion (Kaufman et al., 2015; Westlye et al., 2017).

Interestingly, AAS have a limited capacity for acute intoxication (Fingerhood et al., 1997),
unlike alcohol, cannabis, opioids, or stimulants. AAS are not consumed for an immediate
“high”, but instead taken over time for the latent reward of increased muscularity and
strength (Grönbladh et al., 2016). Nonetheless, AAS users are at risk for developing
dependence, especially with long-term use. Roughly 30% of users demonstrate substance
use disorders as outlined by the Diagnostic and Statistical Manual of Mental Disorders,
fifth edition (DSM 5; Grönbladh et al., 2016; Ip et al., 2015; Kanayama et al., 2010a;
Kanayama et al., 2003; Pope et al., 2014b). AAS users meet criteria for continued use
despite adverse side effects and experience of withdrawal symptoms (Kanayama et al.,
2009a; Kanayama et al., 2020).

9
However, the DSM 5 criteria for substance use disorders poorly capture AAS abuse, since
the criteria refer to substances that are fast-acting and have immediate intoxication
(Hildebrandt et al., 2018). The list of drugs of abuse in the DSM 5 does not include AAS,
instead they fall under the category of “other drugs” (Grönbladh et al., 2016; SAMHSA,
2017). This is problematic for two reasons. First, AAS dependence goes unrecognized and
may affect a greater number of users than previously thought. Second, lack of DSM
recognition of AAS as drugs of abuse hinders the amount of research dedicated to
understanding the cognitive and behavioral costs of AAS dependence (Pope et al., 2014a).
Steps to define androgen intoxication (Hildebrandt et al., 2018) and modify criteria for
recognition of AAS dependence (Kanayama et al., 2009b; Kanayama et al., 2009c) have
benefits for the treatment of dependent users and research regarding the consequences
of abuse.

Studying the negative consequences of AAS abuse in existing users is useful but has
limitations. Preexisting conditions can complicate our understanding of what cognitive
and behavioral outcomes result from AAS use. Also, individual users take a wide variety
of AAS and related agents in various patterns of stacking, cycling, and pyramiding. This
makes it difficult to generalize the consequences of AAS abuse. Furthermore, the majority
of users are secretive and fail to disclose their recreational habits to physicians. Controlled
experiments of AAS with volunteers in the lab also have restrictions, since it is unethical
to give high doses (equivalent to those known to be used by recreational users) of AAS to
naïve users. Therefore, animal models of AAS abuse are useful because researchers can
standardize the type, dose, and method of exposure. Animal studies are a valuable
complement to human studies of AAS, by helping to inform the conclusions drawn from
10
human studies (Clark et al., 1997; Cunningham et al., 2013; McGinnis, 2004; Onakomaiya
and Henderson, 2016; Wood, 2008).

1.3 Rodent models of AAS abuse
Rodent models have provided valuable insight into our understanding of the relationship
between AAS and aggression. Like human users, rodents treated with AAS show increased
aggression (Clark and Henderson, 2003). In rodent models, the degree to which AAS elicit
aggression depends on the specific type and chemical structure of AAS, of which
testosterone produces the greatest response (Breuer et al., 2001; Cunningham et al., 2013;
Farrell and McGinnis, 2003, 2004; Lumia and McGinnis, 2010). Contrary to the belief
that AAS produce indiscriminate aggression (Lumia and McGinnis, 2010) aggressive
responses were observed in testosterone-treated animals only when physically provoked
(Cunningham and McGinnis, 2006; Cunningham and McGinnis, 2007; Lumia et al.,
1994; McGinnis et al., 2002) or in response to predator threat (Clark and Henderson,
2003). In fact, displays of aggressive behavior in rodents treated with AAS depend on
multiple factors, particularly environmental and social cues (McGinnis, 2004).
Researchers suggest that AAS affect aggression by heightening vigilance and decreasing
the threshold necessary for an aggressive response, and not by enhancing baseline
aggressiveness (Cunningham et al., 2013; Cunningham and McGinnis, 2006;
Cunningham and McGinnis, 2007; McGinnis, 2004). These rodent studies support
human findings implicating that AAS cause changes in aggression (Cunningham et al.,
2013; Lumia and McGinnis, 2010; McGinnis, 2004; Nagypál and Wood, 2007).

11
Rodent models of AAS abuse evaluating cognition complement the visuospatial, working
memory, and executive functioning deficits reported in human users (Bjørnebekk et al.,
2019; Kanayama et al., 2013). Tests of visuospatial memory using the Morris water maze
found that rats treated with AAS performed poorly, indicative of impaired visuospatial
memory (Magnusson et al., 2009; Pieretti et al., 2013; Tanehkar et al., 2013). Likewise,
AAS-treated rats have difficulty recognizing familiar objects in a novel object recognition,
a measure of working memory (Bueno et al., 2017). AAS treatment also diminishes social
recognition memory in rats (Kouvelas et al., 2008). Cognitive flexibility, the ability to
easily switch between tasks and respond to change, is one type of executive function
(Logue and Gould, 2014). Testosterone-treated rats are impaired on set-shifting and
reversal learning, measures of cognitive flexibility in rodents (Wallin and Wood, 2015).

Healthy executive functions, like cognitive flexibility and working memory, are important
for optimal cost-benefit analysis when making decisions (Del Missier et al., 2012). Sub-
optimal decision-making and risky behaviors are often reported in human users (Ip et al.,
2015; Middleman et al., 1995), which can indicate impaired executive functioning. In
rodents, an operant discounting task evaluates the effects of AAS on decision-making
behavior. Importantly, the modality of the ‘cost’ associated with the ‘benefit’ (i.e. reward)
can be manipulated, testing different types of decision-making. Testosterone treatment
in rats modifies several types of decision-making, including physical effort, delay,
punishment, and probability. Rats treated chronically with high-dose testosterone show
the willingness to work harder (Wallin et al., 2015), wait longer (Wood et al., 2013), and
accept mild discomfort (Cooper et al., 2014) for a large reward, but are sensitive to reward
uncertainty (Wallin et al., 2015). Nonetheless, it remains unclear how these findings
12
extend to decision-making and risk-taking in humans that depend on cognitive effort.
Since modern society places increasing demands on cognitive, rather than physical effort,
it is important to evaluate the extent to which AAS use negatively impacts cognitive
abilities affecting decision-making. In Chapter 2, I use a novel cognitive effort discounting
paradigm to investigate the effects of AAS on decision-making requiring cognitive effort.

1.4 Androgen receptors in the brain
Animal models have the added benefit of allowing researchers to study the underlying
neurobiology to determine how AAS may affect cognition and behavior. Androgens can
modify behavior in the brain by binding to the androgen receptor (AR). AR is a genomic
receptor that translocates into the nucleus when activated by its ligand, leading to DNA
transcription. Not surprisingly, brain areas responsible for androgen-sensitive behaviors,
like male reproductive behavior and aggression (Aleyasin et al., 2018; Bahrke et al., 1996),
have a high density of AR. These ‘classical’ androgen-responsive brain areas include the
extended medial amygdala (medial amygdala and bed nucleus of stria terminalis), CA1
subfield of hippocampus, lateral septum, medial preoptic area, anterior and ventromedial
nuclei of the hypothalamus (Menard and Harlan, 1993; Simerly et al., 1990; Wood and
Newman, 1999). Since all AAS are synthetic derivatives of testosterone, they mimic the
action of endogenous testosterone in the brain by binding to AR.

In contrast, histological evidence substantiating androgen’s role in complex cognitive
behaviors is less established. As reviewed above, behavioral studies of cognitive flexibility
and decision-making that demonstrate androgen sensitivity depend on the nucleus
accumbens (Acb). Acb is a part of the ventral striatum, located within the basal ganglia.
13
Yet visualizing AR in Acb and prefrontal brain regions known to regulate cognition is
difficult because of the low amount of AR (DonCarlos et al., 2006). Investigation by
researchers using improved immunostaining techniques with greater sensitivity reveals
ARs are more broadly distributed than previously recognized. AR is present rostrally in
the prefrontal cortex (PFC), in medial PFC, and orbital frontal cortex. AR is found in Acb
and the caudate and putamen nuclei of the basal ganglia, and in midbrain structures like
ventral tegmental area (VTA), substantia nigra pars compacta, and substantia nigra pars
reticulata (Kritzer, 2004; Low et al., 2017).

Many of these structures that express AR have projections to Acb (Creutz and Kritzer,
2004). Since Acb is central to motivation and reward, and plays a major role in attention
and decision-making, these androgen-responsive afferents may contribute to the
androgen sensitivity of Acb-dependent behaviors. With greater availability of improved
staining techniques and an accumulating body of evidence that implicates a broader role
for androgens in behavior, it was important to reexamine the distribution of AR and
identify AR-responsive afferents to Acb. This was the focus of Chapter 3.

1.5 Rewarding and reinforcing properties of androgens
The mesocorticolimbic dopamine (DA) pathway is critical for reward. VTA in midbrain
provides direct DA input to both PFC (mesocortical) and Acb (mesolimbic; Mhaouty-
Kodja, 2018; Tobiansky et al., 2018). Acb DA is sometimes colloquially referred to as the
“reward signal’ in the brain (Schultz, 1999). Numerous models of complex cognitive
behaviors that depend on reward, like decision-making and addiction, are facilitated by
DA activity in Acb. As discussed above, decision-making behavior is sensitive to
14
androgens; interactions between androgens and DA in Acb suggest a potential
neurobiological mechanism mediating these effects. Chapter 2 addresses the role of DA
receptors in cognitive effort-based decision-making.

The role of Acb DA in mediating reward is central to studies of addiction and dependence
(Carelli, 2002; Chen et al., 2017; Hyman et al., 2006). Testosterone demonstrates both
rewarding and reinforcing properties that depend on Acb DA; both properties can be
evaluated using condition place preference (CPP) and self-administration. In a CPP task,
both humans and animal subjects prefer a location previously paired with reward (e.g.
food, sex, or drugs). Rats show CPP following intracerebral injections of testosterone into
Acb (Packard et al., 1997). Flupenthixol, a DA receptor antagonist, blocks testosterone
CPP (Packard et al., 1998; Schroeder and Packard, 2000). Systemic AAS administration
and direct AAS implants in Acb also induce CPP in rats (Martínez-Rivera et al., 2015;
Parrilla-Carrero et al., 2009; Rosellini et al., 2001). Animal models of self-administration
have excellent face validity for demonstrating both the rewarding and reinforcing
properties of a drug (Clemens and Holmes, 2018; Sanchis-Segura and Spanagel, 2006;
Spanagel, 2017). Both male and female hamsters readily learn to press a lever or nose-
poke to self-administer a testosterone infusion intravenously or
intracerebroventricularly, even to the point of overdose and death (Dimeo and Wood,
2006; DiMeo and Wood, 2006; Peters and Wood, 2005; Wood, 2002, 2004; Wood et al.,
2004).

The rewarding and reinforcing properties of a drug contribute to the development of
substance abuse and dependency in users. Dependency and relapse are ongoing problems
15
underlying addiction. Notably, exposure to drug cues (people, places, paraphernalia, etc.)
previously associated with drug experience can lead to drug-seeking and relapse. The
ability for neutral cues to become imbued with motivational properties and drive drug-
seeking behavior can result from altered reward learning mediated by Acb DA (Berridge,
2017; Berridge and Robinson, 2016; Robinson et al., 2016). Since most drugs of abuse are
rewarding and reinforcing, including AAS, impaired reward learning can occur that
fosters maladaptive behaviors. However, the potential for drug seeking and relapse in
AAS remains unknown. The focus of Chapter 4 was to determine whether AAS foster
drug-seeking behavior.

1.6 Conclusion
The research presented in the chapters of this dissertation attempt to address gaps in
knowledge regarding the risks of AAS abuse and potential harm for long-term users. The
picture of AAS abuse and its consequences is still incomplete. Chapter 5 details areas of
research and future directions for understanding the neurobiology of AAS abuse,
including androgen interactions with DA, and for evaluating further risks associated with
AAS dependency and relapse. The following three chapters build upon previous work to
extend our understanding of the effects of AAS on cognitive behavior and the potential
neurobiological sites of action.

16
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Chapter 2. Anabolic-androgenic steroids and cognitive effort
discounting in male rats

2.1 Abstract
Anabolic-androgenic steroids (AAS) are drugs of abuse that impair behavior and
cognition. In a rodent model of AAS abuse, testosterone-treated male rats expend more
physical effort, by repeatedly pressing a lever for a large reward in an operant discounting
task. However, since modern society prioritizes cognitive over physical effort, it is
important to determine if AAS limit cognitive effort. Here we tested the effects of AAS on
a novel cognitive-effort discounting task. Each operant chamber had 3 nose-pokes,
opposite 2 levers and a pellet dispenser. Rats pressed a lever to illuminate 1 nose-poke;
they responded in the illuminated nose-poke to receive sugar pellets. For the 'easy' lever,
the light remained on for 1s, and a correct response earned 1 pellet. For the 'hard' lever,
the light duration decreased from 1s to 0.1s across 5 blocks of trials, and a correct response
earned 4 pellets. As the duration of the nose-poke light decreased, all rats decreased their
choice of the hard lever in a modest discounting curve. Task accuracy also decreased
significantly across the 5 blocks of trials. However, there was no effect of testosterone on
choice of the hard lever or task accuracy. Antagonism of dopamine D1 or D2 receptors
had no effect on lever choice or task accuracy. However, serotonin depletion significantly
decreased preference for the hard lever, and impaired task accuracy. Thus, physical effort
discounting depends on dopamine activity, while cognitive effort discounting task is
sensitive to serotonin. AAS impair physical effort discounting, but not cognitive effort
discounting.
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2.2 Introduction
Anabolic androgenic steroids (AAS) are performance-enhancing substances taken to
increase muscle mass and enhance athletic performance. Although AAS have legitimate
medical applications, illicit use is a growing concern with over 4 million users in the
United States alone (Kanayama and Pope, 2018). Importantly, AAS use is no longer
restricted to professional athletes, but now includes both adolescents and young adult
men seeking cosmetic benefits (Kanayama and Pope, 2018; Pope et al., 2014). In this
regard, 22% of AAS users initiate use before age 20, and 85% before age 35 (Pope et al.,
2014). AAS abuse has serious consequences for physiological health, including increased
risk for reproductive, cardiovascular, hepatic, and psychiatric dysfunction (Kanayama et
al., 2018). Deficits in visuospatial memory tasks have been observed in long-term human
AAS users (Kanayama et al., 2013), and early exposure to AAS during adolescence is
especially troubling, since neural circuitry is still undergoing maturation, and AAS
exposure may have a lifelong impact (Blakemore and Choudhury, 2006; Cunningham et
al., 2013; Cunningham and McGinnis, 2007). Our laboratory focuses on AAS-induced
changes in cognition and decision-making in rodents. Early exposure to AAS in
adolescent male rats causes long-term changes in behavior and aggression (Lumia and
McGinnis, 2010). However, the full extent of AAS use on cognitive function remains
unknown. This was the focus of the present study.

Studying the effects of AAS on cognition in human users is challenging, given the diversity
of AAS types, and the unique pattern of dosing and timing by individual users. For this
reason, we model AAS abuse in male rats exposed to chronic high-dose testosterone to
standardize the type, dose, and timing of exposure. Testosterone exposure begins in
38
adolescence at 5 weeks of age (Spear, 2000), and extends through young adulthood to
model human patterns of AAS use (Pope et al., 2014). Testosterone treatment may have
different effects on decision making if not initiated until adulthood. We use testosterone
because it is a popular AAS used recreationally due to its low cost and easy availability,
and because all AAS are synthetic derivatives of testosterone. In our recent studies,
chronic testosterone treatment beginning in adolescence impairs cognitive flexibility
(Wallin and Wood, 2015) and modifies decision-making in operant discounting tasks in
rats (Cooper et al., 2014; Wallin et al., 2015; Wallin-Miller et al., 2018; Wood et al., 2013).
In operant discounting, an animal chooses between two levers, one associated with a
small reward (1 sugar pellet), and the other with a large reward (3 or 4 pellets). The large
reward is discounted by the imposition of a “cost” to make the reward less desirable. As
the cost associated with choice of the large reward increases across blocks of trials,
preference for the lever associated with the large reward decreases, to create a discounting
curve. Compared with vehicle controls, testosterone-treated rats continue to show greater
preference for the large reward lever when the discounting cost is a delay (Wood et al.,
2013), mild punishment (Cooper et al., 2014), or physical effort (Wallin et al., 2015).
However, testosterone-treated rats are more sensitive to reward uncertainty (Wallin et
al., 2015). Therefore, testosterone-treated rats do not favor a "win-at-all-costs" strategy.
To extend these findings, the present study explored the effects of chronic, high-dose
testosterone on the willingness to expend cognitive effort. Studying the effects of AAS on
cognitive effort is important because success in modern society emphasizes cognitive
ability over physical activity.

39
Cocker et al. (2012) developed a rodent cognitive effort task (rCET), where a rat chooses
to respond on a lever associated with an easy or difficult task to earn a small or large
reward, respectively. After selecting a lever, the rat had to respond in the correct nose-
poke, which was illuminated for either 1.0s (easy trial) or 0.2s (hard trial). We adapted
this approach to create a cognitive effort-discounting task, where the cost associated with
the large reward (attention to a progressively shorter light duration) increases in
successive blocks of trials. This task measures two features of cognitive effort: 1) the rat’s
willingness to expend cognitive effort, as determined by their choice of the hard lever
associated with a large reward, and 2) their accuracy in completing the task. Since
testosterone-treated rats will expend more physical effort for a large reward (Wallin et al.,
2015), we hypothesized that they would also expend more cognitive effort.

Many aspects of decision-making behavior depend on the activity of dopamine (DA) D1-
like (D1R) and D2-like (D2R) receptors in the nucleus accumbens (Acb; Ghods-Sharifi
and Floresco, 2010; Hosking et al., 2015; Stopper and Floresco, 2011; Stopper et al., 2013;
Winstanley and Floresco, 2016). Specifically, antagonism of D1R in Acb increases
sensitivity to reward omission in a test of probability discounting (Stopper et al., 2013),
and both D1R and D2R antagonists in Acb reduce operant responding for food in a cost-
benefit test (Nowend et al., 2001). Likewise, AAS increase sensitivity to reward omission
and alter effort-based decision making (Wallin et al., 2015). In parallel with these
behavioral effects, AAS decrease D1R throughout Acb, and selectively reduce D2R in the
shell of Acb (Kindlundh et al., 2001). Accordingly, the present study tested dopaminergic
involvement in cognitive effort discounting in testosterone- and vehicle-treated rats. On
the other hand, D1R and D2R antagonists have no effect on performance in the rCET
40
(Hosking et al., 2015). However, serotonin also plays a role in decision-making. Systemic
serotonin depletion increases impulsivity measured by reduced preference for a large
delayed reward in rats (Denk et al., 2005), and promotes risky decision making in rats
(Zeeb et al., 2009) and monkeys playing a gambling task (Long et al., 2009). Thus, we
also tested serotonergic involvement in cognitive effort discounting using systemic
serotonin depletion.

2.3 Materials and methods
2.3.1 Animals
28 male Long-Evans rats (Charles River Laboratories, MA) were 5 weeks of age at the
start of the study. Rats were pair-housed with access to water ad libitum under a reversed
14L:10D photoperiod. Rats were tested daily (5 d/week) during the dark phase, and all
rats remained gonad-intact in order to approximate human AAS use. To maintain a slow
rate of growth (3-4 g/day) and facilitate operant responding, rats were food restricted as
in our previous studies (Cooper et al., 2014; Wallin et al., 2015). Rats were weighed daily
and received ~15-25 g of chow in their home cage following behavioral testing. Vehicle-
and testosterone-treated groups did not differ in body weight at the start of the study or
throughout testing. With free-feeding, there is no effect of chronic testosterone treatment
on food intake or body weight (Wood et al., 2013). Experimental procedures were
approved by USC’s Institutional Animal Care and Use Committee and were conducted in
accordance with the Guide for the Care and Use of Laboratory Animals, 8th Ed (National
Research Council, National Academies Press, Washington DC; 2011).

41
2.3.2 Testosterone treatment
Starting 2 weeks before behavioral training and throughout testing, rats received
injections s.c. of testosterone (7.5 mg/kg; 4-androsten-17β-ol-3-one; Steraloids, RI) or
aqueous vehicle [3% ethanol and 13% cyclodextrin (RBI, MA)] 5 d/week. This dose
approximates heavy steroid use in humans and has been used previously to demonstrate
the effects of AAS on cognition and decision-making in rats (Cooper et al., 2014; Wallin
et al., 2015; Wallin and Wood, 2015; Wood et al., 2013). At 7.5 mg/kg, testosterone
cypionate causes a ten-fold increase in serum testosterone (>20 ng/ml), vs oil-treated
control rats (ca. 2 ng/ml; Clark et al., 1997). However, it is important to note that chronic
exposure to AAS lowers endogenous testosterone secretion. Injections were administered
immediately prior to behavioral training and testing.

2.3.3 Operant chambers
All training and testing was conducted in operant chambers (Med Associates, VT)
enclosed in sound-attenuating boxes with fans for ventilation. As shown in Figure 1A, one
side of each chamber was outfitted with three nose-pokes with stimulus lights and
infrared beams. The opposite wall had 2 retractable levers flanking a pellet dispenser with
food cup and house light.
42

Figure 1. Schematic of the operant chamber (A) and experimental design (B) for cognitive effort
discounting. (A) Response on the lever (1) illuminates a nose-poke light on the opposite wall. The rat
must respond in the illuminated nose-poke (2) for a correct trial to deliver a reward in the food cup
(3). (B) For choice of the hard lever, the nose-poke light duration decreases across successive blocks
of trials from 1.0 s to 0.1 s; the nose-poke light remains constant at 1.0 s with a response on the easy
lever. A correct response earns 3 sucrose pellets for a hard lever trial, but only 1 pellet when the easy
lever is selected.

2.3.4 Training
Initially, rats were trained in daily 40-min sessions to respond in an illuminated nose-
poke to receive a 45-mg sucrose pellet (Bio-Serv Inc., Frenchtown, NJ). In each trial, one
nose-poke light was illuminated and remained lit until the rat made a response in any of
the three nose-pokes. A correct response in the illuminated nose-poke earned a sucrose
pellet. An incorrect response extinguished the nose-poke light, and the chamber returned
to the inter-trial interval (ITI) state with the house light on for 5s before the start of a new
trial. Once rats demonstrated 90% correct responses, they were required to respond in
the illuminated nose-poke within 30s; response omissions resulted in a 5s ITI.
Subsequently, the nose-poke light duration was reduced to 20s, and then to 10s.

Hard
lever
Easy
lever
1
2
3
1
Choose
Lever
2
3
Receive
Reward
Reward
(pellets):
Hard lever
Easy lever
1.0 0.6 0.4 0.2 0.1 3
Nose-poke light duration (s):
1 1.0
food cup
Respond on
illuminated
nose-poke
nose-pokes
A
B
43
After rats responded in the correct nose-poke within 10s with 90% accuracy, they were
trained to respond on a lever (Figure 1A). One retractable lever was inserted into the
chamber at random and remained extended until the rat responded. Following a lever
response, the lever retracted, the house-light was extinguished, and one of the nose-pokes
was randomly illuminated after a 5s delay. Rats had 10s to respond in the illuminated
nose-poke to receive a pellet. When rats completed at least 60 trials in 40 min with 90%
accuracy, the nose-poke light duration was decreased to 5s, then to 2s, and 1s. Although
the nose-poke light was only illuminated briefly, rats had 5s to make a response before
the trial was recorded as an omission.

Lastly, rats were next trained on reward discrimination, consisting of 60 trials divided
into 5 blocks. Each block included 4 forced-choice trials and 8 free-choice trials. In forced-
choice trials, 1 lever was inserted (2 trials/lever). In free-choice trials, both levers were
available. One lever was defined as the 'easy' lever and the other as the 'hard' lever. The
position for each remained the same throughout training and testing. However, no
cognitive effort cost was imposed for a response on the hard lever during reward
discrimination, and the nose-poke light was illuminated for 1s with a response on either
lever across the five blocks. A correct response on the easy lever delivered 1 pellet; a
correct response on the hard lever delivered 3 pellets. Training was complete when rats
responded on the hard lever on 90% of free-choice trials.

2.3.5 Cognitive effort discounting
Once rats mastered reward discrimination, they were tested for cognitive effort
discounting for 10 days until choice behavior stabilized. As in reward discrimination, each
44
daily session consisted of 60 trials divided into 5 blocks of 4 forced-choice and 8 free-
choice trials. Likewise, correct response in the illuminated nose-poke following choice of
the easy lever delivered 1 pellet; a correct response following choice of the hard lever
delivered 3 pellets. However, the hard lever was now discounted by decreasing the time
the nose-poke light remained illuminated across blocks: 1.0, 0.6, 0.4, 0.2, and 0.1s (Figure
1B). Rats had 90 minutes to complete 60 trials. Responses were considered stable when
there was no effect of time by repeated-measures ANOVA (RM-ANOVA) comparing
choice of the hard lever at each block over 3 days of testing, with time as the repeated
measure, as in Cooper et al. (2014).

2.3.6 Monoamine drugs
2.3.6.1 D1 and D2 receptor antagonists
On the day after baseline choice behavior was established, rats were injected with saline,
and tested for cognitive effort discounting 10 minutes later. Subsequently, all rats were
tested with D1 and D2 receptor antagonists at two doses in a counter-balanced design on
separate test days with a baseline saline test day between injections. To control for
potential additive effects, none of the rats received both doses of the same antagonist on
consecutive days. Saline baseline behavior was evaluated on separate days from
monoamine drug manipulations. DA antagonists were the D1R antagonist R(+)-SCH-
23390 hydrochloride (SCH; #D054 Sigma-Aldrich, St. Louis, MO) and the D2R
antagonist S-(-)-eticlopride hydrochloride (eticlopride; #E101; Sigma-Aldrich). SCH and
eticlopride solutions were prepared fresh daily in 0.9% physiological saline, and were
delivered by injection i.p. 10 minutes prior to behavior. SCH was used at 0.01 mg/kg and
0.03 mg/kg, and eticlopride at 0.03 mg/kg and 0.06 mg/kg. These doses have previously
45
been shown to affect cognition in rats (Floresco et al., 2006; Hosking et al., 2015; St Onge
and Floresco, 2009).

2.3.6.2 Serotonin depletion
After exposure to DA receptor antagonists, rats underwent serotonin depletion with 4-
chloro-DL-phenylalanine (PCPA; 300 mg/kg in saline; #C6506, Sigma-Aldrich). PCPA
acts as an irreversible inhibitor of serotonin synthesis. Rats received PCPA injections i.p.
at 48 and 24 hours before testing. This treatment is sufficient to deplete 90% of serotonin
in the central nervous system (Brigman et al., 2010; Izquierdo et al., 2012). Rats
continued to receive daily injections of testosterone or cyclodextrin vehicle immediately
before training and testing throughout the study.

2.3.7 Data analysis
Data were collected using MedPC software and imported into Microsoft Excel. Statistical
analysis was conducted using SPSS version 20 statistical software. Statistical significance
was set to p<0.05. Saline baseline data were computed from the average of 4 days of
testing (pre-treatment plus 3 rest days). Data on total trials completed, total pellets
earned, and total omissions were evaluated by ANOVA comparing vehicle and
testosterone at saline baseline. Because lever(s) were extended indefinitely in each trial
until the rat made a response, the trial duration was not fixed. Therefore, the total number
of forced-choice and free-choice trials completed in 90 minutes reflects attention to the
operant task. For the monoamine drugs (SCH, eticlopride, PCPA), RM-ANOVA with drug
as the repeated measure (saline vs. drug) was used to test for both drug and testosterone
effects on total trials, total pellets, and total omissions. Each monoamine drug was
46
independently compared to saline. If a significant effect was found, estimate of effect size
was calculated and reported as η
2
, using the appropriate test as specified in Lakens (2013).

Percent choice of the hard lever during choice trials was calculated from the total number
of trials in which the hard lever was selected out of 8 choice trials in each block. Percent
accuracy on the hard lever was calculated from the number of successful trials, out of the
total number of choice trials in which the hard lever was selected in each block. For saline
baseline, choice behavior and task accuracy were analyzed by RM-ANOVA with
testosterone (vehicle vs. testosterone) as the between-subjects factor, and block as the
repeated measure. For the monoamine drugs, choice behavior and task accuracy were
analyzed with a three-factor mixed RM-ANOVA with testosterone (vehicle vs.
testosterone) as the between-subjects factor, and block and drug (saline vs. monoamine
drug) as repeated measures.

At baseline, all rats completed 60 trials in 90 minutes. During treatment with monoamine
drugs, some rats failed to complete all 60 trials. For rats that did not complete any choice
trials in a particular block, no data were recorded for that block, and this reduced the
number of subjects analyzed by RM-ANOVA (see Results).

To determine whether testosterone or monoamine drugs affected sensitivity to reward
delivery and omission, win-stay (WS) and lose-shift (LS) behavior was analyzed on a trial-
by-trial basis. A WS occurred when the rat chose the hard lever and received the large
reward for a correct response (win), and then chose the hard lever again in the following
trial (stay). LS occurred when the rat received no pellets on a hard lever trial (loss), either
47
because of an incorrect response or omission, and then chose the easy lever on the
following trial (shift). WS and LS ratios were computed as the number of times each
behavior occurred divided by the total number of wins or losses respectively. WS and LS
ratios were averaged for vehicle- and testosterone-treated rats in each block and
compared by three-factor mixed RM-ANOVA with testosterone treatment as the between-
subjects factor and block and drug (saline vs. monoamine drug) as the repeated measures.

2.4 Results
2.4.1 Effect of testosterone at baseline
There was no effect of testosterone on task acquisition. Both vehicle- and testosterone-
treated rats required the same number of training sessions to learn the task and
completed all 60 trials within the 90-minute session. Testosterone- and vehicle-treated
rats earned a comparable number of pellets (vehicle: 74.9±6.2 pellets/session vs.
testosterone: 82.1±4.4) and made a similar number of omissions (vehicle: 19.3±2.4 vs.
testosterone: 16.5±1.9) in each session.

For all rats, there was a significant effect of block on choice of the hard lever [F(4,14)=6.99,
p<0.003; η
2
=0.39], but no effect of testosterone and no testosterone x block interaction
by RM-ANOVA. All rats (vehicle- and testosterone-treated) responded less frequently on
the hard lever as the nose-poke light duration decreased (Figure 2A). Rats chose the hard
lever on 89.5±1.8% of free-choice trials at a 1s light duration, but only on 71.9±4.0% of
trials with a 0.1s light duration. Likewise, there was a significant effect of block on task
accuracy [F(4,14)=29.43, p<0.0001; η
2
=0.89], but no effect of testosterone (Figure 2B).
Vehicle- and testosterone-treated rats showed a marked impairment in accuracy on hard
48
lever trials as the nose-poke light duration decreased, from 74.9±2.5% correct when the
duration was 1s to only 28.6±4.3% correct at 0.1s light duration.

As illustrated in Figure 2C, the WS ratio remained unchanged throughout all five blocks
of trials, and there was no effect of testosterone treatment. Although task accuracy
declined steeply as the duration of the nose-poke light decreased, rats retained a strong
preference for the hard lever following a win, with a WS ratio of 0.92±0.02 when the nose-
poke light duration was 1s, and 0.94±0.04 when the duration was 0.1s. Similarly, rats
were disinclined to shift to the easy lever after a loss on the hard lever, demonstrating a
low LS ratio in the first 1.0s block (0.11±0.05). Nonetheless, the LS ratio increased
significantly across blocks [F(4,13)=4.03, p<0.05; η
2
=0.55], to 0.35±0.05 when the nose-
poke light duration was 0.1s (Figure 2D).
49

Figure 2. Behavioral responses on cognitive effort discounting. Hard lever choice (percent choice, A)
and task accuracy (percent correct response, B) in vehicle- (open circles, solid line) and testosterone-
treated rats (closed circle, dotted line). Rats were pretreated with saline. Win-stay and Lose-shift ratios
are shown in C and D. Asterisk indicates significant effect of block (nose-poke light duration) on lever
choice, task accuracy, and Lose-shift ratio.

2.4.2 D1R antagonist, SCH23390
At 0.01 mg/kg, there was no effect of the D1R antagonist SCH on any measure of lever
choice or task accuracy, and no interaction with testosterone (data not shown). At 0.03
mg/kg, SCH significantly reduced the number of trials completed, to 51.5±1.7 trials in 90
min [F(1,17)=6.29 p<0.05; η
2
=0.27], but there was no effect on total pellets received
(71.4±7.0 per session) or total omissions (16.0±2.4).

% Choice: Hard Lever
Vehicle + Saline
Testosterone + Saline
% Correct: Hard Lever
Nose-poke light duration (sec)
Win-Stay Ratio
Lose-Shift Ratio
A B
C D
1.0 0.6 0.4 0.2 0.1 1.0 0.6 0.4 0.2 0.1
100
80
60
40
20
0
100
80
60
40
20
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
* block * block
* block
50
For analysis by RM-ANOVA, only 12 rats (n=6 each, vehicle and testosterone) had data
from all 5 blocks of choice trials; but restricting analysis to the first 4 blocks increased the
sample size to 15 rats (7 vehicle, 8 testosterone). We therefore analyzed the data twice:
with n=12 from all 5 blocks, and again with n=15 including just the first 4 blocks of choice
trials. The results were identical. There was no effect of 0.03 mg/kg SCH on hard lever
choice, and no interaction with testosterone. Both vehicle- and testosterone-treated rats
maintained a strong preference for the hard lever (Figure 3A). However, after SCH
treatment there was no longer an effect of block on hard lever choice. D1R antagonism
had no effect on task accuracy, which decreased significantly for all rats across trial blocks
[F(4,7)=13.80, p<0.002; η
2
=0.89 for 5 blocks] (Figure 3B). Furthermore, there was no
interaction of SCH with testosterone. WS and LS ratios were unaffected (data not shown).

2.4.3 D2R antagonist, eticlopride
At 0.03 mg/kg, the D2R antagonist eticlopride had no significant effect on the number of
trials completed, pellets earned or number of omissions. Likewise, there was no effect of
eticlopride and no interaction with testosterone on hard lever choice or task accuracy. At
0.06 mg/kg, eticlopride decreased the number of choice trials completed (40.7±4.7)
[F(1,17)= 15.95, p<0.001; η
2
=0.43] and pellets earned (50.5±8.3) [F(1,17)=12.70, p<0.005;
η
2
=0.35] for all rats. Although there was a modest decrease in omissions (14.3±2.0 vs.
17.8±1.5 for saline), this did not reach significance [F(1,17)=3.85, p=0.066; η
2
=0.18].

Similar to SCH, there was no effect of 0.06 mg/kg eticlopride on hard lever choice across
5 (n=9) or 4 blocks of choice trials (n=10), and no interaction with testosterone. Following
D2R antagonism there was no longer a significant effect of block on hard lever choice
51
(Figure 3C). Despite this effect, task accuracy decreased as the nose-poke light duration
decreased [F(3,6)=8.32, p<0.015; η
2
=0.81 for 4 blocks; F(4,4)=6.00, p=0.055; η
2
=0.86 for
5 blocks] (Figure 3D), but there was no effect of eticlopride on task accuracy in vehicle-
or testosterone-treated rats. In addition, eticlopride had no significant effect on either WS
or LS ratios (data not shown).

Figure 3. Effects of pharmacological dopaminergic manipulations on behavioral outcomes for
cognitive effort operant discounting. Hard lever choice (percent choice, A, C) and task accuracy
(percent correct response, B, D) shown in vehicle- (open circles, solid line) and testosterone-treated
rats (closed circle, dotted line). Rats were pretreated with the dopamine D1 receptor antagonist SCH-
23390 hydrochloride (SCH) at 0.03 mg/kg (A, B), or the D2 receptor antagonist S-(-)-eticlopride
hydrochloride (eticlopride) at 0.06 mg/kg (C, D). Baseline saline data are shown in gray for
comparison. Asterisk indicates significant effect of block (nose-poke light duration) on task accuracy.

% Choice: Hard Lever
% Correct: Hard Lever
Nose-poke light duration (sec)
A B
C D
1.0 0.6 0.4 0.2 0.1 1.0 0.6 0.4 0.2 0.1
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
% Choice: Hard Lever
% Correct: Hard Lever
Vehicle + SCH23390
Testosterone + SCH23390
Vehicle + Saline
Testosterone + Saline
Vehicle + Eticlopride
Testosterone + Eticlopride
Vehicle + Saline
Testosterone + Saline
* block
* block
52
2.4.4 Serotonin depletion with PCPA
Relative to saline baseline, rats treated with PCPA completed significantly fewer trials
(51.0±2.7) [F(1,17)=8.45, p < 0.01; η
2
=0.33], earned fewer pellets (78.7±3.7 saline vs.
41.3±3.9 PCPA) [F(1,17)=166.12, p<0.0001; η
2
=0.91], and had more response omissions
(17.8±1.5 saline vs. 27.6±2.4 PCPA) [F(1,17)=15.585, p<0.001; η
2
=0.48] in each 90-min
session. Even so, there was no significant interaction of testosterone with PCPA.

Across all 5 blocks of trials (n=10), PCPA treatment marginally decreased choice of the
hard lever [F(1,8)=4.81, p=0.06; η
2
=0.38] as shown in Figure 4A, although this did not
reach significance. Furthermore, there was no effect of block on choice of the hard lever.
When analysis was restricted to the first 4 blocks (n=14), PCPA significantly decreased
choice of the hard lever [F(1,12)=9.88, p<0.01; η
2
=0.45]. As shown in Figure 4B, serotonin
depletion significantly reduced task accuracy [F(1,8)=115.05, p<0.0001; η
2
=0.93 for 5
blocks], and eliminated the effect of block. Overall, serotonin depletion significantly
decreased choice of the hard lever and impaired accuracy. However, there was still no
effect of testosterone-treatment on either measure. We were unable to compute WS or LS
ratios because of incomplete data.

53

Figure 4. Effect of serotonin depletion on behavioral outcomes for cognitive effort operant
discounting. Hard lever choice (percent choice, A) and task accuracy (percent correct response, B) for
vehicle- (open circles, solid line, n=4) and testosterone-treated rats (closed circle, dotted line, n=6).
For serotonin depletion, rats were pretreated with 4-chloro-DL-phenylalanine (PCPA, 300 mg/kg) at
24 and 48h before testing. Baseline saline data are shown in gray for comparison. Dagger indicates
significant effect of PCPA on task accuracy.

2.5 Discussion
The present study used a novel cognitive effort discounting task to determine if chronic
exposure to high-dose testosterone affects the willingness or ability to expend cognitive
effort to obtain a food reward. As the duration of the nose-poke light decreased, response
accuracy declined, along with preference for the hard lever to obtain a large reward.
However, testosterone had no effect on lever preference or accuracy. The lack of an effect
of testosterone on cognitive effort discounting contrasts with our recent finding (Wallin
et al., 2015) that testosterone-treated rats are willing to expend more physical effort (i.e.
make more lever presses) to obtain a large reward. Furthermore, although physical effort
discounting is sensitive to DA manipulations via both D1R and D2R (Salamone et al.,
2009; Salamone et al., 2012), cognitive effort discounting in the present study was
unaffected by the DA antagonists SCH and eticlopride. Instead, serotonin depletion with
PCPA reduced preference for the hard lever and impaired performance on hard lever
† PCPA
% Choice: Hard Lever
% Correct: Hard Lever
A B
100
80
60
40
20
0
100
80
60
40
20
0
Nose-poke light duration (sec)
1.0 0.6 0.4 0.2 0.1 1.0 0.6 0.4 0.2 0.1
Vehicle + PCPA
Testosterone + PCPA
Vehicle + Saline
Testosterone + Saline
54
trials. These findings highlight the selective effects of testosterone on DA-dependent
decision-making and emphasize that cognitive and physical effort discounting may be
mediated by different neurochemical systems.

Our cognitive effort discounting task is modified from the rodent cognitive effort task
(rCET) of Cocker et al. (2012). In both tasks, the rat chooses between two levers to select
trial difficulty, i.e. the nose-poke light duration. In rCET, the duration in a high-effort trial
is fixed at 0.2s. By contrast in the present study, the duration decreases from 1.0-0.1s
across successive blocks. In their study, Cocker et al. (2012) divided their subjects into
'workers' who preferred the high-effort/high-reward lever, and 'slackers' who preferred
the low-effort/low-reward lever. Interestingly, we did not find separate populations of
‘workers’ and ‘slackers’ in the present study; all rats preferred the hard lever. This could
be due either to the greater visuospatial cognitive demands of rCET (choice of 5 nose-
pokes vs. 3 in cognitive effort discounting) and/or the larger reward in our task (3 pellets
vs. 2 pellets in rCET). Thus, compared with rCET, cognitive effort discounting in the
present study favors a response on the hard lever. Even so, there was still a significant
effect of block on both hard lever choice and accuracy at baseline. This suggests that
testosterone- and vehicle-treated rats adjust their responses similarly in accordance with
the likelihood of success. The lack of effect of testosterone or testosterone x block
interaction argues that chronic high-dose testosterone does not influence choice behavior
or performance on cognitive effort discounting.

Cognitive effort discounting extends our understanding of the impact of AAS on decision-
making in a rat model of AAS abuse. From our previous studies, testosterone-treated rats
55
will work harder (physical effort discounting; Wallin et al., 2015), wait longer (delay
discounting; Wood et al., 2013), and accept more discomfort (punishment discounting;
Cooper et al., 2014) to earn a large reward, compared with vehicle-treated controls. Rats
treated chronically with high-dose testosterone also show impairments in cognitive
flexibility, as measured by set-shifting and reversal learning (Wallin and Wood, 2015).
However, testosterone-treated rats are relatively less tolerant of uncertainty (probability
discounting; Wallin et al., 2015), their preference for a large uncertain reward is lower
than that of controls.

Cognitive effort discounting is intriguing because it shares elements in common with both
physical effort and probability discounting. Similar to physical effort discounting,
cognitive effort discounting requires the rat to complete a task after selecting the large
reward lever in order to earn a reward. In physical effort discounting, testosterone-treated
rats selected the large reward lever more often and completed more trials, compared with
vehicle controls (Wallin et al., 2015). In contrast, testosterone- and vehicle-treated rats in
the present study showed equivalent preference for the hard lever, with similar numbers
of omissions. These findings suggest that cognitive effort and physical effort may be
mediated by different neurochemical circuits, as discussed below. Secondly, like
probability discounting, choice of the hard lever in cognitive effort discounting does not
guarantee reward delivery. In probability discounting, rats learn to 'play the odds' by
estimating the probability of reward. They then use this information to select the
appropriate lever. For cognitive effort discounting, receipt of the large reward depends on
the rat’s ability to successfully complete the task, where lever choice is informed by the
rat's estimate of his own 'skill'. Since AAS users describe feelings of invincibility (van
56
Amsterdam et al., 2010; Vassallo and Olrich, 2010), we expected that chronic high-dose
testosterone might cause rats to overestimate their skill in cognitive effort discounting,
and thereby choose the hard lever even when it was no longer advantageous to do so. This
was not the case, and there was no difference between vehicle- and testosterone-treated
rats. Instead our findings add to a growing literature to suggest that AAS-induced changes
in behavior (e.g. AAS-induced aggression, colloquially known as 'roid rage) are not
irrational and impulsive, but are rather an outcome produced by a different weighing of
costs and benefits.

WS and LS ratios for cognitive effort discounting offer additional insight into how rats
perceive their skill on the visuospatial task to inform their choice. There was no effect of
block on the WS ratio in the present study. Rats continued to prefer the hard lever (72%
of trials at 0.1s duration), especially following a win on the previous trial (94%), even
when the likelihood of success was low (29%). By contrast, the WS ratio decreased across
blocks in probability discounting (Wallin et al., 2015). Specifically, with a comparable
chance of success (25%), rats chose the large reward lever on only 51% of trials, including
85% of trials following a win. Differences between the two discounting tasks may account
for the differences in WS ratios. After choosing a lever, cognitive effort discounting
requires an active response by the rat to earn the large reward, whereas receipt of the large
reward in probability discounting is passive. Therefore, choosing the large reward lever
in cognitive effort discounting presumably reflects the rat's estimation of his ability to be
successful in the task.

57
The LS ratio for both probability discounting and cognitive effort discounting increased
significantly across blocks, indicating that rats are sensitive to reward omission in both
tasks. However, the LS ratio in the present study remained relatively low. Specifically, at
a 0.1s nose-poke light duration, the LS ratio was 35% vs. 50% for probability discounting
when the probability of reward was 25% (Wallin et al., 2015). Together, the higher WS
ratio and lower LS ratios with cognitive effort discounting suggest that rats may
overestimate their own abilities when the outcome depends on skill (cognitive effort
discounting) compared to chance (probability discounting). This has parallels with the
human tendency to overestimate their skill in games (Kwak, 2016), and value rewards
more when they have to work hard to obtain them (Zentall, 2015).

The lack of an effect of testosterone on cognitive effort discounting was somewhat
surprising, considering that AAS alter DA receptors in the nucleus accumbens (Acb), and
that Acb is critical for decision-making (Ghods-Sharifi and Floresco, 2010; Stopper and
Floresco, 2011). The Acb core subregion is especially important for rCET and physical
effort discounting (Hosking et al., 2015). On the rCET, inactivation of Acb core with
baclofen/muscimol severely impaired rats’ performance by significantly reducing the
number of trials initiated, and tended to decrease choice of the high effort/high reward
lever (Silveira et al., 2018). For physical effort discounting, inactivation of Acb core
reduced selection of the high effort lever (Ghods-Sharifi and Floresco, 2010). Likewise,
stimulating DA activity systemically with amphetamine decreased physical effort
expenditure, as did systemic treatment with either the DA receptor antagonist
flupenthixol (Floresco et al., 2008), SCH or eticlopride (Hosking et al., 2015). AAS
increase D2 receptors in Acb core (Kindlundh et al., 2001) and increase preference for the
58
large reward lever in physical effort discounting (Wallin et al., 2015). In contrast with
physical effort discounting, performance on rCET was not affected by systemic
administration of SCH or eticlopride (Hosking et al., 2015). This is in keeping with the
present study, where there was no effect of systemic treatment with either D1R or D2R
antagonists. We cannot rule out the potential for local effects of DA receptor antagonists
in Acb core on cognitive effort discounting. Nonetheless, our results and those of Hosking
et al. (2015) suggest that cognitive effort-based decision making is relatively less sensitive
than physical effort discounting to systemic DA manipulations.

Although SCH and eticlopride did not alter preference or accuracy with the hard lever,
they did significantly reduce the number of trials completed in 90 minutes. This is in line
with the results of Hosking et al. (2015) for rCET, where SCH and eticlopride reduced the
number of completed trials, and increased response and choice omissions. In both the
present study and rCET, response omissions occur when the rat fails to respond in a nose-
poke within 5s after selecting an easy or hard trial. In rCET, choice omissions occur when
the rat fails to respond on either lever within 10s. In rCET, rats were tested for 30 min in
each session. By contrast, choice omissions did not occur in the present study, because
there was no time limit on the lever response. This simplified the analysis of responses
across multiple blocks. However, delays in choosing a lever prevented some rats from
completing all 60 trials in a 90-min session. 90 min was a generous time allowance,
considering that all 60 trials could be completed in less than 18 min with optimal
performance. Because some rats treated with SCH or eticlopride did finish all 60 trials, it
is unlikely that motor ability was substantially impaired. Instead, it seems more likely that
the DA antagonists reduced engagement in the task.
59

While DA had no effect on cognitive effort discounting, serotonin depletion with PCPA
significantly decreased the number of trials completed, decreased preference for the hard
lever, impaired task accuracy, and increased response omissions. The 300 mg/kg dose
was selected based on previous studies that found no effect on reversal learning (Brigman
et al., 2010), or performance in a physical effort task (Denk et al., 2005). Furthermore,
the decrease in choice of the hard lever cannot simply be explained by a decrease in
preference for a large reward. Previous studies have shown that PCPA-treated rats
continue to prefer a large reward in a physical effort T-maze task, but not in a delay task
unless the delay costs were matched for both the high and low reward (Denk et al., 2005;
Izquierdo et al., 2012). This suggests that serotonin depletion does not affect motivation
to expend physical effort but does contribute to reduced tolerance for delay (Denk et al.,
2005). The implication for cognitive effort discounting in the present study is that the
decrease in choice of the hard lever following PCPA-treatment may be due primarily to
impaired task performance. Previous work has found that serotonin depletion impaired
performance in both novel object recognition and spontaneous alternation tasks (du
Jardin et al., 2014; Pehrson et al., 2012). More so, short-term and spatial-working
memory in rats is impaired by lesions of serotonergic neurons with the serotonin specific
neurotoxin 5,7-DHT (Hritcu et al., 2007). Deficits in short-term and spatial-working
memory as a result of serotonin depletion could explain the marked decrease in task
accuracy in cognitive effort discounting, with subsequent reduced choice of the hard lever.

Nonetheless, PCPA did not reveal effects of testosterone on cognitive effort discounting.
AAS abuse has serious psychological and behavioral effects, including changes in mood
60
and aggression (Cunningham and McGinnis, 2007; McGinnis, 2004). The
neuromodulator serotonin and its various receptor subtypes may play a role in mediating
many of these behavioral effects of AAS (Keleta et al., 2007; Kindlundh et al., 2003;
McGinnis, 2004). Animal models of AAS abuse have found changes in receptor densities
for the 5-HT1b and 5HT2 receptor (Kindlundh et al., 2003), and lower levels of serotonin
in the basal forebrain and dorsal striatum (Lindqvist et al., 2002). AAS have also been
shown to downregulate serotonin receptor messenger RNA in the prefrontal cortex and
amygdala (Ambar and Chiavegatto, 2009). The relationships among serotonin levels,
specific receptors, AAS and aggression have been well studied (Bonson et al., 1994;
Bonson and Winter, 1992), with lower levels of serotonin correlated with increased
aggression (Grimes and Melloni, 2005; Keleta et al., 2007; Morrison et al., 2015a, b; Ricci
et al., 2012). Considering the impact of AAS on serotonin receptor expression, it was
reasonable to expect that serotonin depletion might reveal effects of testosterone on
cognitive effort discounting. Because testosterone- and vehicle-treated rats responded
similarly to PCPA, our results instead highlight the overall importance of serotonin
availability for cognitive effort discounting. Ultimately, although chronic high-dose
testosterone leads to changes in DA and serotonin systems in the brain, these changes do
not affect performance on cognitive effort discounting. Instead, cognitive effort
discounting as presented here is unaffected by DA antagonism, but sensitive to serotonin
depletion.

61
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Chapter 3. Androgen receptor-positive afferents to the nucleus
accumbens in male rats

3.1 Abstract
Recent evidence finds a broader role for androgens in modulating cognitive behaviors that
rely on the mesocorticolimbic dopamine (DA) pathway. The nucleus accumbens (Acb) is
an important structure in this pathway for decision-making, reward, and reinforcement
learning. Androgens are rewarding, and supraphysiologic levels can modify decision-
making involving DA activity in Acb. Activation of the androgen receptor (AR) may be the
mechanism by which androgens mediate behavior, although how androgens directly
access Acb to alter decision-making remains poorly understood. The present study
utilized enhanced immunohistochemistry using tyramide signal amplification (TSA) to
determine if ARs are found in Acb, and to evaluate the distribution of AR-positive
afferents to Acb, labeled with the retrograde tracer cholera toxin beta (CTB). With TSA,
we found improved AR staining throughout the brain, particularly in Acb shell. Additional
ARs were visible in CA1 of dorsal and ventral hippocampus, and in layers II/III of cortex,
including the mPFC. CTB labeled inputs to Acb shell were observed in CA1 of ventral
hippocampus, ventral tegmental area, bed nucleus of the stria terminalis (BST), and
mPFC, particularly in infralimbic cortex. Lastly, a small number of AR-positive afferents
to Acb shell were found in infralimbic cortex in sections double-labelled for AR and CTB.
Abundant double-labeling was present in the CA1 region of ventral hippocampus and
BST. TSA-enhanced AR staining revealed AR-positive neurons in Acb and demonstrated
widespread AR-sensitive afferents to Acb from cortical and subcortical regions. These
69
findings suggest potential mechanisms through which androgens may modulate decision-
making.

3.2 Introduction
Androgens have a well-established role in stimulating male reproductive behavior and
aggression. The high density of androgen receptor (AR)-containing neurons in limbic and
hypothalamic regions reflects the role of androgens in social behaviors (Newman, 1999).
These classical androgen-responsive brain regions include the medial amygdala (Me), bed
nucleus of stria terminalis (BST), lateral septum (LS), medial preoptic area (MPO), and
anterior (AH) and ventromedial nuclei of the hypothalamus (VMH). Recent studies have
identified cognitive behaviors that are sensitive to androgens, implicating a broader role
for androgens beyond sex and aggression. Androgens affect complex cognitive behavioral
tasks, including set-shifting and reversal learning (Wallin and Wood, 2015), and operant
decision-making paradigms (Cooper et al., 2014; Wallin et al., 2015; Wood et al., 2013).
Many of these cognitive behaviors require the mesocorticolimbic pathway, which includes
the prefrontal cortex (PFC) and medial (mPFC), orbitofrontal cortex (OFC), nucleus
accumbens (Acb), and ventral tegmental area (VTA; Mhaouty-Kodja, 2018; Tobiansky et
al., 2018). Acb is the central hub that integrates signals from both cortical and midbrain
structures in the mesocorticolimbic pathway to mediate behavior. Neurons of the
mesocorticolimbic pathway traditionally have relatively few ARs, and it has been difficult
to visualize AR immunoreactivity (AR+) in these brain regions (DonCarlos et al., 2006).
With more sensitive staining techniques available (Low et al., 2017), we can reexamine
the distribution of AR+ cells in Acb and Acb afferents.

70
Acb receives dopamine (DA) projections from VTA, as well as descending excitatory
glutamatergic input from mPFC. Acb is comprised of core (AcbC) and shell (AcbSh)
subregions, which have different connectivity and behavioral contributions (Deutch and
Cameron, 1992; Floresco, 2015; Zahm, 1999). Briefly, AcbC extends around the anterior
limb of the anterior commissure and lies adjacent to the caudate-putamen (CPu). AcbC
has connections with basal ganglia and is involved predominantly in motor control.
AcbSh sits ventromedial to AcbC and has greater connectivity with steroid-sensitive
regions of the hypothalamus and limbic system involved in social behaviors and
cognition. Mesencephalic innervation of Acb maintains a medial-lateral topographic
organization from VTA and substantia nigra (SN), with preferential innervation of AcbSh
(Groenewegen et al., 1999; van Dongen et al., 2008; Wouterlood et al., 2018). Previous
studies have combined AR immunoreactivity with retrograde tract tracing to identify
androgen-sensitive afferents to Acb. Tracer injections in medial Acb (along the border of
AcbC and AcbSh) colocalize with AR+ cells in medial VTA, while injections in lateral Acb
(including AcbC and ventrolateral AcbSh) co-label cells in lateral VTA (Creutz and Kritzer,
2004). Despite expression of AR throughout cortex, few cortical afferents to AcbC or
AcbSh express AR (Kritzer, 2004). Instead, AR+ cells and retrogradely-labeled neurons
are found in the same pyramidal layers, but not in the same cells.

Since these studies, improved immunostaining methods have been developed which offer
greater sensitivity to detect ARs. These techniques include antigen retrieval (Jiao et al.,
1999) and tyramide signal amplification (Adams, 1992; Low et al., 2017). The present
study combines retrograde tract tracing in Acb and enhanced staining for AR to
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investigate the distribution of AR+ cells in Acb subregions, and co-localization of AR-
responsive afferents to Acb in cortical and subcortical structures.

3.3 Materials and methods
3.3.1 Animals
8 male Long-Evans rats were purchased from Charles River Laboratory (Wilmington,
MA, USA) at 5 weeks of age. Rats were pair-housed throughout the study under a reversed
14L:10D photoperiod with food and water available ad libitum. Experimental procedures
were approved by USC’s Institutional Animal Care and Use Committee and were
conducted in accordance with the Guide for the Care and Use of Laboratory Animals, 8th
Ed (National Research Council, National Academies Press, Washington DC; 2011).

3.2.2 Tracer injection
To visualize afferents to AcbC and AcbSh, rats received iontophoretic injections of the
retrograde tract tracer cholera toxin beta (CTB, List Biological Laboratories, Campbell,
CA), as in Wood and Swann (2005). CTB was prepared as a 0.5% solution in 0.1 M
phosphate buffered saline (PBS), and delivered under stereotaxic guidance. Rats were
anesthetized with ketamine/xylazine (100 and 10 mg/kg, respectively) i.p. and received
Meloxicam SR (4.0 mg/kg) s.c. as an analgesic. Each rat was positioned in a stereotaxic
apparatus (David Kopf Instruments, Tujunga, CA) with lambda and bregma in the same
horizontal plane. CTB was deposited by iontophoresis through a glass micropipette (tip
diameter 14-20 µm) with a constant current source (Midguard, Stoelting Co., Wood Dale,
IL) which delivered a +7 µA current pulsed at 7 sec intervals on/off for 10 min.
72
Coordinates for AcbC were AP: +1.4 mm, ML: ±1.1, and DV: -7.2 relative to bregma.
Coordinates for AcbSh were AP: +1.4 mm, ML: ±0.8, and DV: -7.2. The micropipette was
left in place for 5 min after injection.

3.2.3 Perfusion and sectioning
7-10 days after surgery, rats were deeply anesthetized and perfused transcardially with
200 ml 0.1 M phosphate buffered saline (PBS) containing 0.1% sodium nitrate for
vasodilation, followed by 300 ml of 4% paraformaldehyde in PBS. Brains were removed,
and post-fixed overnight in 4% paraformaldehyde. The next day, brains were
cryoprotected in 30% sucrose before being sectioned coronally at 40 µm in a series of 12
on a freezing microtome, collected in PBS with 0.01% sodium azide as a preservative, and
stored at 4
˚
C. Sections were immunostained for AR with and without TSA amplification,
for CTB alone, or double-labelled for CTB plus AR with TSA amplification. All
incubations and washes were performed at room temperature with gentle agitation.

3.2.4 CTB immunohistochemistry
Immunostaining for CTB was as in Wood and Swann (2005). Briefly, sections were
incubated overnight in goat anti-CTB (1:10,000: List Biological Laboratories) with 4%
normal donkey serum and 0.3% Triton X-100. The next day, sections were exposed to
biotinylated secondary antibody (donkey anti-goat, 1:400, Jackson Immunoresearch,
Malvern, PA) followed by avidin-biotin-horseradish peroxidase (ABC, 1:800, Vectastain
ABC Elite Kit, Vector Laboratories, Burlingame, CA), each for 1 hr with extensive washes
in 0.1 M PB between. Horseradish peroxidase was visualized with NiCl-enhanced 3,3'
73
diaminobenzidine (DAB) as the chromogen to produce a blue-black reaction product over
labeled neurons and fibers. The DAB reaction was terminated by extensive washes in PB.
Sections were mounted on gelatin-coated slides, dehydrated, and coverslipped with
Permount (Fisher Scientific, Pittsburgh, PA).

3.2.5 AR immunohistochemistry
Immunostaining for AR with TSA amplification was modified from Low et al. (2017), with
the addition of antigen retrieval to enhance staining. First, sections were incubated 30
min in 2% H2O2 to block endogenous peroxidase. For antigen retrieval, sections were
placed in 10 mM sodium citrate buffer with 0.05% Tween 20, pH=6.0 at 70-80
o
C,
followed by 1% sodium borohydride in PB at room temperature, each for 15 min. Next,
sections were exposed to 8% tryptone in PB for 1 hr to reduce non-specific staining, before
incubating overnight in rabbit anti-AR (1:200; ab133273; Abcam, Inc., San Francisco,
CA). The next day, sections were exposed to biotinylated donkey anti-goat (1:400,
Jackson Immunoresearch) for 1 hr, followed by ABC for 30 min, with extensive washes in
PB between. Sections were washed in PB and transferred to a solution of biotinylated
tyramide with 0.01% HCl for 10 min. Biotinylated tyramide was prepared as described in
Adams (1992). Sections were washed and returned to ABC for an additional 65 min before
DAB staining. Staining, mounting, and coverslipping was as described above. Additional
sections stained for AR without TSA amplification skipped biotinylated tyramide, and
remained in ABC for 95 min, before washing in PB, and staining with DAB.

3.2.6 AR-CTB Double-labeling
Sections immunostained for AR with TSA amplification were then stained additionally
74
for CTB, except that NiCl was omitted from the DAB solution, producing a tan reaction
product over the cytoplasm of CTB-immunoreactive (CTB+) neurons.

3.2.7 Data analysis
Sections were examined with an Olympus BH-2 microscope equipped with a Canon EOS
6F camera. The location of the CTB injection site was determined for each rat. Three
injections were restricted to AcbC (representative example in Figure 5A) and four were
located in AcbSh (Figure 5B). One rat was excluded because the injection site was on the
border of LS and AcbSh. The distribution of cells labeled with CTB was illustrated for each
rat in a series of camera lucida drawings of relevant brain sections, then summarized for
each injection site (AcbC or AcbSh). In brain regions with CTB+ cells, we evaluated AR+
cells with and without TSA (Figure 6). Distribution of AR+ cells with TSA were identified
(Figure 7A, D, and G) in brain regions that had CTB labeled neurons following injections
in AcbC (Figure 7B, E, and H) and AcbSh Figure 7C, F, and I). A schematic was made to
illustrate the distribution of AR relative to the CTB labeled neurons in specific brain
regions (Figure 8). Double-labeled sections were examined to identify CTB labeled
neurons that were also AR+ and to confirm suspected double-labeled cells identified in
the schematic (Figure 8). An example of a double-labeled cell for AR- and CTB-
immunoreactivity is shown in Figure 5C; showing a representative AR+ afferent to AcbC
from IL. Brain nomenclature is according to Paxinos and Watson (2013).

75

Figure 5. Representative retrograde tracer injections in nucleus accumbens core (AcbC) and shell (AcbSh),
and a representative androgen receptor (AR)-responsive afferent to AcbC core.Retrograde tracer injections
were made into AcbC or AcbSh in order to identify Acb afferents that express AR. The injection sites for
AcbC (A) and AcbSh (B) in two representative cases are shown here. The dark stained area shows the
location and size of the injection. AR+ afferents to Acb (C) were identified by staining for both the retrograde
tracer CTB and AR with tyramide signal amplification (TSA). The double labeled cell here was found in IL
cortex following a CTB injection in AcbC. The black arrow indicates a neuron that is co-labeled for AR and
CTB. The white arrow points to a neuron that is stained for CTB alone and the gray arrows indicate nuclear
staining for AR alone. Scale bar represents 300 µm (A,B) and 100 µm (C). Abbreviations: ac, anterior
commissure.
A
B
C
AcbC
AcbSh
ac
76

Figure 6. Visualization of androgen receptors (AR) is enhanced when combined with tyramide signal
amplification (TSA). AR are detectable without TSA in brain regions with AR expression (A,C).
Addition of TSA amplifies the detection of AR in regions with typically low signal, like AcbC and AcbSh
(B), and in brain areas with high AR, like BST and MPN (D). Scale bar represents 300 µm.
Abbreviations: ac, anterior commissure; AcbC, nucleus accumbens core; AcbSh, nucleus accumbens
shell; BST, bed nucleus of the stria terminalis; f, fornix; MPO, medial preoptic nucleus; TSA, tyramide
signal amplification.

BST f
MPO
A B
C D
TSA- TSA+
AcbC
AcbSh
ac
77
3.4 Results
3.4.1 Retrograde tracer injections in AcbC
The distribution of CTB+ afferents to AcbC complements that previously described in
previous tract tracing studies (Bagot et al., 2015; Britt et al., 2012; Brog et al., 1993;
Groenewegen et al., 1999; Montaron et al., 1996; Zahm and Brog, 1992). CTB injections
produce staining of the cytoplasm, shown by the white arrow in Figure 5C. CTB injections
in AcbC produced retrograde labeling in mPFC, including DP, IL, PrL, and Cg1. In
particular, CTB+ afferents to AcbC originate in layers III and V of PrL and IL, with the
majority found in PrL (Figure 7B; Groenewegen et al., 1999). There were additional
labeled neurons laterally in AI and in orbitofrontal cortex (OFC), including LO and VO.
Caudally, in the paralimbic cortex at the level of pc, AcbC received innervation from PRh
and LEnt, and APir.

AcbC receives abundant projections from subcortical areas including ventral striatum
(AcbSh, VP), and amygdala nuclei BL and BM. In the thalamus, projections to AcbC arise
from anterior PV and PT at the level of the rostral head of hippocampal CA3, and more
caudally from CM and MD at the level of LHb (Figure 7E). In midbrain, SNR and VTA
send scattered projections to AcbC. However, CTB+ labeling in SNR was sparse. Likewise,
AcbC acquires only a modest projection from VS and hippocampal CA1 (Figure 7H).
Instead, AcbC receives afferents from cortical regions connected to the hippocampal
formation, such as PRh and LEnt.
78

Figure 7. Androgen receptors (AR) are expressed in several brain regions that also have direct input
to the nucleus accumbens (Acb). AR are observed in both the prelimbic (Prl) and infralimbic (IL)
cortices, in cortical layers II/III (A). Both Acb core (AcbC; B) and shell (AcbSh; C) have direct
projections from Prl and IL cortices. AcbC appears to receive projections from both superficial layers
II/III and deep layers V/VI from PrL and IL. In contrast, Acb Sh has fewer direct projections from PrL
and IL, with the majority primarily originating in the deep layers V/VI of cortex. Although some AR is
expressed in paraventricular thalamus (PV; D), these appear to be separate from neurons in PV that
have direct projections to AcbC (E) or AcbSh (F). CA1 subfield of ventral hippocampus (G) and
subiculum show dark staining for AR. AcbC appears to have fewer direct projections from ventral CA1
field of hippocampus and ventral subiculum (H) then AcbSh (I), and as a result, less double labeling
A B C
D E F
G H I
IL
PrL
PV
MD
CA1
AHiPM
AR+ AcbC AcbSh
PT
DP
79
is observed. Scale bar represents 200 µm. Abbreviations: AHipM, amygdalohippocampal area
posteromedial part; DP, dorsal peduncular cortex; MD, mediodorsal thalamic nucleus; PT, paratenial
thalamic nucleus.

3.4.2 Retrograde tracer injections in AcbSh
As with AcbC, the distribution of CTB+ afferents to AcbSh also resembles earlier reports
of AcbSh connectivity (Brog et al., 1993; Groenewegen et al., 1999; Zahm and Brog, 1992).
Like AcbC, AcbSh receives afferents from mPFC, as previously reported (Britt et al., 2012;
Brog et al., 1993). In particular, AcbSh preferentially receives afferents from IL (Montaron
et al., 1996; Zahm and Brog, 1992), while AcbC afferents arise predominantly from PrL.
Likewise, afferents to AcbC or AcbSh arise from different cortical layers (Groenewegen et
al., 1999). Specifically, we only find CTB+ afferents to AcbSh chiefly in layer V of IL
(Figure 7C), while AcbC has projections from layers III and V (Figure 7B). Furthermore,
AcbSh has no CTB+ cells in LO or VO of OFC (not shown). Caudally, AcbSh receives
abundant innervation from ventral CA1 of hippocampus, VS, and AHi (Figure 7I). Ventral
CA1 is known to preferentially project to AcbSh (Britt et al., 2012).

In ventral forebrain, AcbSh receives numerous projections from Cl, LS and VP, and from
LH. In dorsal thalamus at the level of LHb, thalamic nuclei project to AcbSh chiefly from
PV and some from MD (Figure 7F). Laterally in amygdala, afferents to AcbSh
predominantly arise from BL and BM (Bagot et al., 2015). In midbrain, AcbSh receives
innervation from both VTA and SNR Groenewegen et al., 1999).

80
3.4.3 TSA-enhanced immunoreactivity for AR
As in earlier studies (Dimeo and Wood, 2006; Kritzer, 2004; Kritzer, 1998; Low et al.,
2017; Tobiansky et al., 2018), neurons immunoreactive for AR have dense staining
restricted to the cell nucleus (Figure 5C; gray arrow). Furthermore, when stained for AR
without TSA amplification, the distribution of labeled neurons was similar to previous
reports (Menard and Harlan, 1993; Simerly et al., 1990; Wood and Newman, 1999).
Specifically, there was abundant staining for AR in limbic and hypothalamic regions that
form nodes in the mammalian social behavior network (Newman, 1999). These include
the extended medial amygdala (Me and BST), LS, MPO, AH, and VMH. AR staining in
BST and MPO is shown in Fig 2C. Other brain regions with dense AR-immunoreactivity
include PMV and CA1 of hippocampus. Without TSA enhancement, we observed few AR+
cells in prefrontal cortex or in AcbC and AcbSh. Staining for AR in Acb without TSA is
shown in Figure 6A.

TSA enhanced AR staining throughout the brain, in both cortical and subcortical areas,
as previously reported (Low et al., 2017). MPO and BST have robust AR staining without
TSA enhancement (Figure 6C). As shown in Figure 6D for MPO and BST, AR staining was
intensified with TSA in brain regions that have abundant AR+ neurons. Additional brain
areas with sparse AR staining in the absence of TSA (e.g. Acb in Figure 6A), show more
robust AR-immunoreactivity in the presence of TSA, as for Acb in Figure 6B. Nonetheless,
TSA-enhanced AR-immunoreactivity remains selectively distributed in brain regions
previously reported to have AR expression (Kritzer, 2004; Low et al., 2017; Simerly et al.,
1990). For example, we did not find AR-positive cells in BL after TSA-enhanced staining.

81
Figure 7A, D, and G shows the distribution of AR+ cells with TSA enhancement in brain
regions that project to AcbC and AcbSh. In cortical areas, TSA revealed substantial
numbers of AR+ cells throughout mPFC, with the majority of AR+ cells with TSA are
found in cortical layers II/III and V/VI of PrL and IL (Figure 7A). AR-immunoreactivity
was enhanced with TSA in OFC, including VO and LO, and in the adjacent AI. As observed
without TSA (Kritzer, 2004). Caudally, in paralimbic cortex, TSA revealed AR+ cells in
PRh, and LEnt at all levels of the hippocampus. With TSA, AR-immunoreactivity is robust
throughout the rostro-caudal extent of VS and hippocampal CA1, including both the
dorsal and ventral divisions (Figure 7G). Likewise, AR staining is improved in thalamic
nuclei that project to Acb (PVA and MD, Figure 7D). More so, TSA enhanced staining also
revealed some AR+ cells in VTA and SNR (not shown), areas with minimal AR-
immunoreactivity when stained without TSA.

3.4.4 AR-CTB colocalization
Although there are AR+ neurons in regions with CTB+ afferents to AcbC and AcbSh,
represented by a schematic in Figure 8, we found minimal colocalization of AR+ with
CTB+ in individual neurons when confirmed with the double-labeled sections. With
tracer injections in AcbSh, ventral CA1, VS, and AHi had the largest number of CTB+
neurons with AR staining (Figure 8F). For AcbC injections, limited double-labeled
neurons were observed in these areas (Figure 8E). Instead, we saw a few AR+ AcbC
afferents in LEnt and PRh. Rostrally in PrL and IL, there were only a handful of double-
labeled AR+ afferents to AcbC (double-labeled cell in Figure 1C; schematic of AR and CTB
labeling in Figure 8A) or AcbSh (Figure 8B). Because ARs in cortex are primarily
expressed in pyramidal neurons from layers II/III (Kritzer, 2004), it is not unexpected
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that few cortical neurons projecting to Acb are AR+. No double-labeling was observed in
the amygdala. Midline thalamic nuclei including anterior portion of PV, MD, and PT have
CTB+ afferents to AcbC (Figure 7E) and AcbSh (Figure 7F), and AR+ staining (Figure 7D).
Although some of these AR+ and CTB+ cells appear to be colocalized in the schematic
(Figure 8C and D), no double-labeling was confirmed in the corresponding histological
sections (not shown).
83

Figure 8. Schematic representing the distribution of androgen receptor (AR)-immunoreactive cells
relative to CTB-labeled neurons. AR-positive cells (black circles) were identified in brain regions with
direct input to nucleus accumbens core (AcbC) and shell (AcbSh). CTB-immunoreactive neurons (open
triangles) are identified following tracer injections in AcbC (A, C, and E) or AcbSh (B, D, and F).
Drawings of AR- and CTB-immunoreactivity were combined; colocalization (fuchsia triangles) was
confirmed with the corresponding double-labeled histological sections. A handful of neurons in Prl
and IL double-labeled AR+ afferents to AcbC (A) and AcbSh (B). Although it appears that there is
colocalization in PV and MD for both AcbC (C) and AcbSh (D), no-double labeling was observed. The
largest number of CTB+ neurons with AR staining were found in CA1 of ventral hippocampus and in
AcbC AcbSh
A B
C D
E F
IL
PrL
PV
MD
CA1
AHiPM
PT
DP
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the amygdalohippocampal area posteromedial part (AHipM) following AcbSh injections (F). AcbC has
less double-labeled cells in CA1 (E) and classically has fewer direct projections from this area. Scale
bar represents 200 µm. Abbreviations: DP, dorsal peduncular cortex; MD, mediodorsal thalamic
nucleus; PT, paratenial thalamic nucleus; PV, paraventricular thalamus.

3.5 Discussion
The present study evaluates the distribution of AR-immunoresponsive Acb afferents in
brain areas involved in cognition (i.e. PFC) using double-labeled chromogenic IHC.
Antigen retrieval combined with TSA improves sensitivity for AR detection. As with
previous reports, TSA prominently increases AR staining intensity throughout the brain,
in both cortical and subcortical structures (Low et al., 2017). Enriched AR staining reveals
a greater distribution of AR+ cells in non-classical sites, like PFC, OFC, AI, and Acb. AR
staining in classical sites with numerous ARs, such as in limbic and hypothalamic
structures, is darker and more pronounced with TSA. Improved staining does not reveal
AR everywhere; AR remains absent from BLA. Other brain regions with Acb afferents,
like mPFC and ventral CA1, have AR+ cells, but few afferents show double-labeling
despite the enhanced AR staining. The distribution of Acb afferents differs depending on
the specific locus of the injection site in AcbC or AcbSh. We observe connectivity of Acb
subregions that is consistent with previous reports (Brog et al., 1993; Groenewegen et al.,
1999; Zahm and Brog, 1992).

The presence and wider distribution of ARs in the mesocorticolimbic pathway denotes
androgen sensitivity in brain regions associated with cognition and decision-making.
These findings confirm and expand on previous reports of AR distribution in non-
classical brain regions (Low et al., 2017). AR is present in multiple nodes of the
85
mesocorticolimbic pathway, and within brain areas interconnected with this brain
network. Behaviors mediated by this pathway are androgen sensitive. The
mesocorticolimbic pathway plays a significant role in decision making, cognitive
flexibility, and memory (Cooper et al., 2014; Wallin et al., 2015; Wallin and Wood, 2015).
Classically, androgens act in limbic and midline hypothalamic nuclei to mediate social or
reproductive behaviors. The presence of ARs in non-limbic and non-hypothalamic
structures supports recent observations that androgens have a broader modulatory role
in complex cognitive behaviors.

With TSA enhancement, we found more AR in AcbSh than AcbC, suggesting that AcbSh-
mediated behaviors may be more sensitive to local AR activity than AcbC. In this regard,
decision-making for an uncertain reward depends on DA in AcbSh and is sensitive to
high-dose androgens (Wallin et al., 2015; Wallin-Miller et al., 2018). In contrast, motor
impulsivity depends on AcbC, but seems to be unaffected by high-dose androgens (Cooper
et al., 2014). In support of these behavioral effects, high-dose androgens modify DA
receptors in AcbC and AcbSh (Kindlundh et al., 2001). Likewise, high-dose testosterone
decreased dendritic spine density in AcbSh without affecting total spine number (Wallin-
Miller et al., 2016). Compared to AcbC, AcbSh is more interconnected with brain regions
rich in AR. This may reinforce a greater androgen sensitivity for AcbSh mediated
behaviors than AcbC.

Despite the widespread distribution of AR in brain regions that connect to Acb, it is
remarkable that afferents to Acb failed to colocalize with AR staining. This lack of co-
localization suggests that androgens have an indirect modulatory effect on decision-
86
making. This is consistent with androgen effects on behavior. Unlike excitatory or
inhibitory neurotransmitters which have a direct causal effect, hormones have more of a
modulatory role on behavior. In short, hormones do not drive behavior, but bias the
likelihood of a behavior to occur. In this manner, androgens act as neuromodulators.
Therefore, lack of AR co-localization on Acb afferents does not rule out a role for
androgens in these behaviors. Because AR is still present in Acb and in brain regions that
project to Acb, androgens can act locally in these brain regions to influence behavior, even
without co-localizing on Acb afferents.

In the mPFC, we observed ARs and Acb afferents in separate cortical layers. With TSA
enhancement, AR was present predominantly in the supragranular layers (layers II/III)
of mPFC, as reported previously (Kritzer, 2004). The supragranular layers have both
inhibitory interneurons and small- to medium-sized excitatory pyramidal cells. In
particular, supragranular pyramidal cells make interhemispheric connections within the
same layer, and with cells in the infragranular layers (layers V/VI). Pyramidal cells of
layer V preferentially project to the basal ganglia, brain stem nuclei, and spinal cord
(Mountcastle, 1997; Shipp, 2007), and this is where we found retrograde labeling with
CTB. Nonetheless, expression of AR in the supragranular layer provides a means by which
androgens may modulate neurocircuitry involved in cognition, since layer III pyramidal
cells project to cells in layer V. This pattern of spatial segregation continues in Cg1, Rsp,
and Per of the allocortex. However, in Ent and Pir, AR+ cells are sparse and scattered
throughout all major cortical layers. In supragranular layers of Ent and Pir, Kritzer
identifies AR+ cells as both small pyramidal neurons and parvalbumin- and calbindin-
immunoreactive interneurons (2004).
87
Despite the dense distribution of AR throughout hippocampal CA1 and ventral subiculum,
few AcbSh afferents in ventral CA1 expressed AR. Dorsal hippocampus has no direct Acb
afferents and is involved primarily in learning and memory. Ventral CA1 is involved in
stress and emotional affect, and provides numerous afferents to AcbSh. We observed only
a limited number of AcbSh afferents in ventral CA1 that are AR+. This is surprising, given
the abundant AR+ in CA1, and the direct connectivity with AcbSh. However, ventral
hippocampus has reciprocal projections to mPFC and ventral subiculum (Fanselow and
Dong, 2010; Li et al., 2015; Maren and Fanselow, 1995). These projections provide an
alternative means by which androgens may modulate cognition and social behaviors.

As reported previously (Kritzer, 2004), some Acb afferents in VTA are AR+. Nonetheless,
we found fewer Acb afferents in VTA than expected, based on previous tract tracing
studies (Breton et al., 2019; Ikemoto, 2007; Taylor et al., 2014). VTA provides the primary
DA innervation to Acb, and DA activity is critical for reward learning and decision-
making. We therefore expected to see more CTB labeling. Furthermore, of the few Acb
afferents in VTA, co-localization with AR was extremely limited. The relative lack of AR
in VTA, and the abundance of AR in Acb suggest that androgens are more likely to act
directly in Acb to mediate DA function. The greater density of AR in cortical and
subcortical areas with direct projections to Acb may also have a more prominent role in
modulating DA levels, compared with VTA.

AR is present in PV, a critical relay point in thalamus for wakefulness, attention, and
feeding behaviors through its central location and interconnectivity with cortical,
subcortical, and brain stem structures (Millan et al., 2017). The presence of AR in PV
88
suggests the potential for androgens to influence these behaviors. But again, we do not
observe co-localization of AR in Acb afferents. Since thalamic nuclei lack interneurons,
these AR+ cells are unlikely to be interneurons. Instead, thalamic pyramidal neurons that
project to non-Acb locations in the brain are the most likely neural population expressing
AR. The primary targets of PV efferents include ventral striatum, CeA, and BST. From
single-neuron tracing studies, PV also has collateral projections from neurons whose
axons bifurcate and synapse on multiple targets (Mitchell and Chakraborty, 2013). This
mechanism could allow widespread coordination of signals that influence a broad range
of cortical structures. According to this concept, androgens can act locally in PV to
modulate widespread networks that do connect with Acb. Similar to PV, thalamic nucleus
MD also plays a role in cognitive functions and the presence of ARs denotes a site for
androgenic action in this thalamic nucleus.

However, not every nucleus in the mesocorticolimbic pathway expresses AR. In the
amygdala, AR was absent from BLA, and we observed few in CeA even with improved IHC
sensitivity. In agreement with previous reports, we saw AR+ cells largely restricted to
MeA, which controls androgen-sensitive social behaviors such as reproduction and
aggression (Keshavarzi et al., 2014). The neurocircuitry between MeA and androgen-
responsive hypothalamic nuclei (VMH, AH, and LH) mediates these behaviors, and is
well-established (Goodson et al., 2005; Newman, 1999). In contrast, BLA is functionally
distinct from MeA, and primarily involved in cognitive behaviors. Lack of AR in BLA and
limited expression in CeA indicates that androgens are not acting at this level of the
neurocircuitry to affect cognition.

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3.6 Conclusion
Using enhanced staining we found widespread AR+ cells in Acb and in the telencephalon,
diencephalon, and discrete areas of midbrain. The broad distribution of AR+ cells in
cognitive circuits emphasizes regions where androgens can have local androgenic action.
Despite few AR+ afferents in cortex and thalamus, androgens can act directly in Acb and
indirectly through projections to Acb to mediate Acb-dependent behaviors.
90
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Chapter 4. Pavlovian conditioned approach in male rats and
anabolic-androgenic steroids

4.1 Abstract
Anabolic androgenic steroids (AAS) are drugs of abuse that impair dopamine (DA)-
dependent cognition and decision-making behaviors. Pavlovian conditioned approach
(PCA) depends on DA and is associated with drug-seeking and reward learning. In the
present study, we determined if AAS alter PCA in rats treated with high-dose testosterone
(7.5 mg/kg) as a model of AAS abuse. PCA trial begins with a brief (8 s) presentation of a
cue (lever) before the delivery of a sucrose pellet. During the lever presentation, some rats
preferentially engage with the lever (sign-tracking, ST), while others interact with the food
cup (goal-tracking, GT). ST correlates with increased drug-seeking and self-
administration. 8 testosterone- and 8 vehicle-treated rats were tested on PCA for five
days. Contrary to our expectations, 12 rats (6 testosterone and 6 vehicle) showed ST, and
no rats met the criteria for GT. Due to the lack of GT, it was not possible to draw
conclusions about testosterone’s effects on PCA. The high proportion of ST in our study
could reflect the strain of rat (Long-Evans vs. Sprague-Dawley) and the cue (lever vs.
tone). Replication of this study in Sprague-Dawley rats with a tone cue would be useful
for evaluating AAS effects on PCA, to determine the potential for relapse with AAS.

97
4.2 Introduction
Anabolic-androgenic steroids (AAS) are drugs of abuse taken to increase muscle mass and
athletic performance, but long-term use has serious negative consequences on
physiology, cognition, and behavior (Pope et al., 2014). Moreover, the number of users
continues to increase, with over 4 million users in the United States (Kanayama and Pope,
2018). Importantly, AAS abuse is no longer restricted to professional athletes, with
growing use among young men and adolescents. Similar to other drugs of abuse, AAS
users are at risk for developing both dependency and maladaptive behaviors, including
impaired cognition and decision-making (Hildebrandt et al., 2014; Kanayama et al., 2013;
Kaufman et al., 2015). Notably, roughly 30% of AAS users develop dependency
(Grönbladh et al., 2016; Kanayama et al., 2009; Pope et al., 2014) and use of other illicit
substances is common among AAS users (Dodge and Hoagland, 2011). However, to what
extent the cognitive changes following long-term AAS use contribute to drug-seeking and
promote relapse is unknown. Relapse is an ongoing problem in drug users where
exposure to drug cues (people, places, paraphernalia, etc.) can motivate drug-seeking and
drug-taking behavior. Dysregulation of reward learning in AAS users may underly one
aspect of addiction, leading to relapse. This is the focus of the present study.

Since it is unethical to administer AAS to humans at high doses, we use chronic high-dose
testosterone administration in rats to model AAS abuse. Testosterone is a popular AAS
because of its low cost and easy availability (Wood and Stanton, 2012). Only male rats are
included in our study because the lifetime prevalence for human AAS use is higher among
adolescent boys (4-6% lifetime prevalence) compared to girls (1.5%-3%; Harmer, 2010).
Rodent models of AAS abuse demonstrate both reward and reinforcement (Mhillaj et al.,
98
2015; Pope et al., 2017; Wood, 2004; Wood et al., 2004), including voluntary self-
administration, tolerance and sensitization (Wood, 2002; Wood et al., 2004), and
conditioned place preference (Packard et al., 1997; Packard et al., 1998). Furthermore,
high-dose testosterone has negative consequences for cognition and decision-making that
depend on dopamine (DA) signaling in the nucleus accumbens (Acb; Wallin-Miller et al.,
2018). Furthermore, AAS alter DA receptors in Acb (Kindlundh et al., 2001; Kindlundh
et al., 2003). Yet, it remains unknown whether changes in DA caused by AAS contribute
to differences in stimulus-reward learning that promotes drug-seeking behavior
associated with relapse.

The ability of drug cues to promote drug-seeking leading to relapse is part of the incentive
salience theory of addiction (Robinson and Berridge, 1993, 2008). Briefly, drug cues are
imbued with incentive salience through repeated paired presentation with the drug. Once
learned, exposure to drug cues imbued with incentive salience can generate “wanting”
and motivate behavior, thereby promoting drug-seeking or drug-taking, leading to
relapse in human users (Berridge and Robinson, 2003; Robinson and Berridge, 2001). In
rats, Pavlovian conditioned approach (PCA) is one method to study individual differences
in stimulus-reward learning and cue-motivated behavior. A cue (e.g. a lever) is presented
briefly (8 s) prior to the delivery of a food reward. The reward is delivered irrespective of
the rat’s behavioral response to the cue. While the cue is available, some rats preferentially
interact with the food cup (goal-tracking, GT), while others primarily interact with the
lever (sign-tracking, ST; Fitzpatrick et al., 2013; Flagel et al., 2009). ST is considered to
be a result of impaired reward learning, since the neutral cue acquires incentive salience.
Sign-trackers are more attracted to drug cues (Saunders and Robinson, 2011), and show
99
more robust drug- and cue-induced reinstatement of drug-seeking behavior for cocaine
(Flagel et al., 2009; Saunders and Robinson, 2010, 2011, 2012; Saunders et al., 2013).

Understanding the degree to which AAS promote ST is important for understanding the
potential for relapse, since sign-trackers may be more susceptible to addiction (Kawa et
al., 2016). Furthermore, acquisition and expression of ST is dependent on DA activity in
Acb, and AAS affect DA receptors in Acb (Kindlundh et al., 2001; Wallin-Miller et al.,
2018). Therefore, we hypothesized that chronic high-dose testosterone would increase ST
in rats.

4.3 Materials and methods
4.3.1 Animals
24 male Long-Evans rats (Charles River Laboratories, MA) were 5 weeks of age at the
start of the study. Rats were pair-housed with a partner from the same treatment group,
with access to water ad libitum. Rats were trained and tested during the dark phase under
a reversed 14L:10D photoperiod. They remained gonad-intact to approximate human
AAS use. To facilitate operant responding, they were weighed daily, and received 25-35 g
of chow/day, equivalent to 10% of BW. Vehicle- and testosterone-treated groups did not
differ in BW at the start of the study or throughout testing. Experimental procedures were
approved by USC’s Institutional Animal Care and Use Committee and were conducted in
accordance with the Guide for the Care and Use of Laboratory Animals, 8th Ed (National
Research Council, National Academies Press, Washington DC; 2011).

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4.2.2 Testosterone treatment
Starting 2 weeks before behavioral training and throughout testing, rats received
injections s.c. of testosterone (7.5 mg/kg; Steraloids, RI) or aqueous vehicle [3% ethanol
and 13% cyclodextrin (RBI, MA)] 5 d/week. Injections were administered immediately
prior to placement in the operant chamber. This dose approximates heavy steroid use in
humans and has been used previously to demonstrate the effects of AAS on cognition and
decision-making in rats (Cooper et al., 2014; Wallin et al., 2015; Wallin and Wood, 2015;
Wood et al., 2013). At 7.5 mg/kg, testosterone cypionate causes a ten-fold increase in
serum testosterone (>20 ng/ml), vs oil-treated control rats (ca. 2 ng/ml; Clark et al.,
1997). However, it is important to note that chronic exposure to AAS lowers endogenous
testosterone secretion.

4.3.3 Operant chambers
All training and testing were conducted in operant chambers (Med Associates, VT)
enclosed in sound-attenuating boxes with fans for ventilation. Each chamber was
outfitted with a house-light, and a retractable lever adjacent to a food cup connected to a
pellet dispenser. An infrared beam detected head entries to the food cup. As in Fitzpatrick
and Morrow (2016), tension on the lever was reduced from 25 g to 10 g to increase
response sensitivity.

4.3.4 Training and testing
Training and testing were adapted from Fitzpatrick and Morrow (2016). Rats received 2
days of training, where they learned to retrieve 45 mg chocolate-flavored sucrose pellets
(Bio-Serv Inc., Frenchtown, NJ) from the food cup in 25 trials on a variable interval (VI)
101
90 s schedule (range: 30-150 s). Each trial began with a VI delay, after which 2 pellets
were dispensed into the food cup. Rats were required to consume all 50 pellets before PCA
testing began. If not, rats received 1 additional day of training. The house-light remained
on throughout training and testing.

Testing was similar to training. After the VI delay, the lever was inserted into the chamber.
After 8 s, the lever retracted, and 2 pellets were dispensed. Behaviors recorded in each
trial included the number of lever presses and latency to first lever press, and the number
of food cup entries and latency to first entry. In addition, we recorded food cup entries
during the VI delay before lever insertion as a measure of investigative activity. Rats were
tested in 25 trials/d for 5 consecutive days, at which point individual PCA index scores for
each rat stabilize (Fitzpatrick and Morrow, 2016).

4.3.5 Data analysis
Statistical analysis was conducted using SPSS version 20 (IBM Corp, Armonk, NY).
Statistical significance was set to p<0.05. For each rat on each test day, lever presses and
food cup entries were used to calculate response bias, latency score, and probability
difference, according to Fitzpatrick and Morrow (2016). Each of these measures is on a
scale of -1.0 to +1.0, with positive values reflecting preference for the lever over the food
cup. Response bias is the total number of lever presses less the total number of magazine
entries, divided by the sum of both. Latency score is the average latency to first food cup
entry less the latency to first lever press, divided by 8 s. Probability difference is the
probability of pressing the lever (number of trials with lever response/25) less the
probability of entering the food cup (number of trials with food cup entry/25). The PCA
102
index score (-1.0 to +1.0) reflects the average of response bias, latency score, and
probability difference (Fraser et al., 2016; Lovic et al., 2011; Meyer et al., 2012). ST is
represented by a PCA index score ≥ +0.5. GT gives a PCA index score ≤ -0.5. Rats in the
intermediate group have PCA index scores between -0.49 and +0.49. All behavioral
measures were averaged for testosterone- and vehicle-treated rats on each day and
compared by RM-ANOVA with time as the repeated measure. If a significant effect was
found, estimate of effect size was calculated and reported as η
2
, using the appropriate test
as specified in Lakens (2013). Where there was a main effect of testosterone or a
testosterone x time interaction, differences between testosterone- and vehicle-treated rats
on individual days wer1`qe compared by Student's t-test.

4.4 Results
There was no effect of testosterone on lever responses or food cup entries, as shown in
Figure 9. Overall, interaction with the lever increased over time, while interaction with
the food cup remained limited. Both vehicle- and testosterone-treated rats made
equivalent numbers of lever presses (Figure 9A), with comparable latencies (Figure 9C),
and demonstrated a similar probability of approaching the lever (Figure 9E). By RM-
ANOVA, all rats (vehicle- and testosterone-treated) significantly increased their
responding on the lever across five days of testing [F(4,11)=7.514, p<0.01; η
2
=0.73], from
34.3±8.0 lever presses in 25 trials on day 1, to 104.1±14.3 presses on day 5. There was a
significant decrease in latency to approach the lever [F(4,11)=15.369, p<0.0001; η
2
=0.85],
from 6.2±0.3 s on day 1 to 3.6±0.4 s on day 5. The probability of responding on the lever
also increased significantly [F(4,11)=12.549, p<0.0001; η
2
=0.82]. On day 1, rats responded
on the lever in 39.8±7.2% of trials but responded on 82.3±6.1% of trials on day 5. In
103
contrast, there was no significant change over time in food cup entries while the lever was
available. Rats made relatively few entries into the food cup (12.9±4.2 entries in 25 trials,
Figure 9B), with an average latency of 6.9±0.3 s (Figure 9D). Likewise, the probability of
food cup entry was modest (26.0±6.4%, Figure 9F).

104

Figure 9. Recorded behavioral measures during stimulus presentation for Pavlovian conditioned
approach. Total number (A, B), average latency (C, D), and probability (E, F) of lever presses (left) and
food cup entries (right) for vehicle- (open circles, solid lines) and testosterone-treated rats (closed
circles, dashed lines) across five days of testing. Asterisk indicates significant effect of time.

0
20
40
60
80
100
0
20
40
60
80
100
0
2
4
6
8
10
0
2
4
6
8
10
0
30
60
90
120
150
0
6
12
18
24
30
Number per day Latency (s) Probability (%)
Lever Press Food Cup Entry
1 2 3 4 5 1 2 3 4 5
Time (day)* Time (day)
A
C
E
B
D
F
Vehicle
Testosterone
105
Not surprisingly, there was no effect of testosterone on response bias (Figure 10A),
latency score (Figure 10B), or probability difference (Figure 10C). For all rats, scores were
consistently positive throughout the 5 days of testing, reflecting a bias to respond on the
lever. The latency score increased significantly over time [F(4,11)=10.38, p<0.001;
η
2
=0.79], from 0.07±0.06 on day 1 to 0.44±0.07 on day 5. Likewise, the probability
difference also increased across 5 days of testing [F(4,11)=7.859, p<0.01; η
2
=0.74], from
0.08±0.10 on day 1 to 0.63±0.08 on day 5. Response bias showed a similar trend: from
0.26±0.16 on day 1 to 0.78±0.09 on day 5, although this did not reach significance
[F(4,11)=2.891, p=0.073; η
2
=0.51].

Lastly, there was no difference in PCA index scores between vehicle- and testosterone-
treated rats (Figure 11A). Mean PCA index scores were positive on day 1 (0.14±0.10), and
increased significantly [F(4,11)=5.382, p<0.01; η
2
=0.66] to 0.61±0.08 on day 5. Based on
individual PCA index scores (Figure 11B), 4 rats (2 each, testosterone and vehicle)
qualified for ST on day 1, and 1 vehicle control met criteria for GT. The rest (n=11) were
intermediate. On day 5, 6 rats from each group met ST criteria, and 2 each were
intermediate (n=4). No rats qualified for GT in the last two days.

During the VI period between trials, the number of food cup entries in 25 trials did not
differ between vehicle- and testosterone-treated rats, as illustrated in Figure 11C. Food
cup entries decreased significantly [F(4,11)=15.165, p<0.0001; η
2
=0.85], from 211.7±27.2
entries on day 1 to 117.0±16.4 on day 5.

106

Figure 10. Calculated behavioral measures for lever preference over food-cup during stimulus
presentation for Pavlovian conditioned approach. Response bias (A), latency score (B), and probability
difference (C) for vehicle- (open circles, solid lines) and testosterone-treated rats (closed circles,
dashed lines) across five days of testing. Asterisk indicates significant effect of time.
Time (day)
A
B
1 2 3 4 5
C
Vehicle
Testosterone
Time*
Time*
Response Bias
-1.0
-0.5
0.0
0.5
1.0
Latency Score
-1.0
-0.5
0.0
0.5
1.0
Probability Difference
-1.0
-0.5
0.0
0.5
1.0
107

Figure 11. Average and individual PCA index scores and food-cup entries during non-stimulus period
for Pavlovian conditioned approach. PCA index score (A) for vehicle- (open circles, solid lines) and
testosterone-treated rats (closed circles, dashed lines) across five days of testing are calculated from
the average of the response bias, latency score, and probability difference for each rat individually (B).
Gray dashed line shows the cutoff for sign-tracking (ST) and goal-tracking (GT). Total number of food
cup entries during the variable interval (VI) period between trials (C). Asterisk indicates significant
effect of time.

-1.0
-0.5
0.0
0.5
1.0
ST
GT
0
100
150
200
250
300
50
1 2 3 4 5
Time (day)*
A
PCA Index Score Food Cup Entries per VI
C
Vehicle
Testosterone
B
-1.0
-0.5
0.0
0.5
1.0
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
108
4.5 Discussion
The present study tested if high-dose testosterone increases ST in PCA, as a model of
drug-seeking behavior. Instead, regardless of testosterone treatment, most rats
demonstrated ST, and no rats met criteria for GT. Across 5 days of testing, all rats
increased preference for and interaction with the cue lever, with no change in the
interaction with the food cup. The lack of GT responses prevents us from drawing
conclusions about testosterone’s effects on PCA. The high incidence of ST in the present
study is most like due to the preference of Long-Evans rats for a lever cue in PCA.

PCA attempts to capture the individual differences in vulnerability to relapse in former
drug users. Cues associated with drug-taking, e.g. people, places, and drug paraphernalia,
may contribute to relapse by generating craving, leading to drug-seeking. Former
smokers report cravings in situations where they used to smoke, such as after a meal or
when driving their car. However, not all drug users are susceptible to drug cues, or go on
to develop a crippling addiction. There is variability in human users in the degree to which
they assign incentive salience to reward cues (Garofalo and di Pellegrino, 2015; Mahler
and de Wit, 2010). Like human drug users, rodents also vary in their tendency to attribute
incentive salience to reward cues, as reflected by the range of ST and GT responses in PCA
(Flagel et al., 2009; Flagel et al., 2008; Flagel et al., 2007; Robinson et al., 2014). We
therefore hypothesized that testosterone would bias the development of ST.

Several important variables may contribute to ST in the present study, including the
modality of the reward cue and the strain of rats. Previous PCA studies have determined
that a lever acquires more motivational properties (more ST) than an auditory cue
109
(Beckmann and Chow, 2015; Meyer et al., 2014; Robinson and Flagel, 2009). Therefore,
use of a lever in our study may have enhanced the development of ST responses (Brown
and Jenkins, 1968; Gamzu and Williams, 1973). Because our initial pilot study found a
high proportion of ST, we modified the PCA procedure of Fitzpatrick and Morrow (2016)
to promote GT. Specifically, we eliminated the cue light over the lever, and increased the
reward to two sucrose pellets. However, these changes were not sufficient to generate a
range of ST and GT responses.

Rat strain differences may be a key explanation for the lack of GT in the present
experiment. Previous reports found variability among different rat strains in their
likelihood to develop ST. Compared with Fischer rats, inbred Lewis rats have greater ST
responses. Enhanced ST in Lewis rats correlates with greater impulsivity, as well as an
increased propensity to self-administer drugs (Kearns et al., 2006). More recently,
Fitzpatrick and colleagues (2013) found differences in PCA behavior among outbred rats
of the same strain (Sprague-Dawley) from different vendors (Harlan and Charles River).
They also found disparities between different colonies from the same vendor. Long-Evans
rats are preferred for behavioral experiments that require greater cognitive demands. For
example, Long-Evans rats require fewer sessions to learn operant tasks relative to
Sprague-Dawley rats, and demonstrate increased locomotion and more exploratory
behavior (Turner and Burne, 2014). In part, this may reflect poor vision in albino
Sprague-Dawley versus pigmented Long-Evans rats, resulting in a slower rate of learning
for tasks that depend on visual information (Charng et al., 2011; Kumar et al., 2015). In
addition, strain differences in cognitive behavior may emphasize underlying genetic
differences in vulnerability to drug sensitivity or enhanced drug self-administration
110
(Cadoni, 2016; Deiana et al., 2007; Fattore et al., 2007; O'Connor et al., 2011). Our lab
uses Long-Evans rats to evaluate AAS effects on cognition because of the complexity of
decision-making behavior. However, Long-Evans rats may not be the ideal strain for PCA,
since they are more likely than albino rat strains to be sign-trackers.

In terms of brain mechanisms, DA is necessary for expression of both ST and GT
responses. However, ST is more sensitive to DA manipulations during acquisition.
Systemic administration of the non-selective DA antagonist flupenthixol abolishes both
acquisition and expression of ST, but only attenuates expression of GT responses (Di
Ciano et al., 2001; Flagel et al., 2011; Roughley and Killcross, 2019; Saunders and
Robinson, 2012). Sign-trackers exhibit greater DA release in response to reward cues than
goal-trackers (Flagel et al., 2011) and enhancing DA release during learning with
amphetamines increases ST, but not GT (Danna et al., 2013).

DA D1-like (D1R) and D2-like (D2R) receptors in Acb mediate ST and GT responses. In
particular, the activity of D2R in Acb core appears to be important for the development of
ST, while D1R contribute to both ST and GT. Blockade of DA receptors in Acb core with
flupenthixol inhibits ST but not GT (Flagel et al., 2011). The D1R antagonist SCH39166
inhibits acquisition of ST and GT responses. In contrast, the D2R antagonist eticlopride
impairs both acquisition and expression of ST, but only blunts expression of GT (Chow et
al., 2016; Roughley and Killcross, 2019).

Applying PCA to a rat model of AAS abuse continues our long-standing exploration of
how high-dose testosterone impacts mesolimbic DA related to reward and decision
111
making. Like PCA, high-dose testosterone is associated with both changes in DA receptors
in Acb core, and cognitive behaviors mediated by Acb core. AAS decrease D1R and
increase D2R in Acb core (Kindlundh et al., 2001). DA in Acb core mediates decision
making under conditions of increased physical effort (Ghods-Sharifi and Floresco, 2010).
Testosterone-treated rats will expend more physical effort to obtain a food reward (Wallin
et al., 2015), and the D2R antagonist eticlopride blocks this effect (Donovan et al., 2019).
Although the association of ST with D2R inhibition in Acb core would appear to contradict
the effects of AAS to increase D2R, it appears that development of ST or GT depends on a
balance of D1R and D2R in Acb (Gillis and Morrison, 2019). Factors that upset this
balance may interfere with learning of reward cues.

Modeling addiction in animals is challenging. Conditioned place preference, self-
administration, and intracranial self-stimulation test the rewarding and reinforcing
properties of drugs. Another approach is to model individual components of addiction,
and the behaviors that contribute to those components. Drug-seeking behavior is an
important aspect of dependence because drug cues or incentive stimuli can motivate
drug-seeking, leading to relapse (Robinson et al., 2014; Zhang et al., 2009). For cues to
motivate behavior and become incentive stimuli, the reward cue must have three key
features. First, the cue needs to be attractive. PCA models how a cue paired with a food or
drug reward becomes attractive to foster ST (Flagel and Robinson, 2017; Meyer et al.,
2012). Second, the cue must be desirable. Will the subject work for access to the cue alone
in the absence of reward? Pavlovian-to-instrumental transfer is a complement to PCA that
addresses this question. Sign-trackers learn to nose-poke for access to a previously-
learned reward cue (lever), even in absence of reward (Everitt and Robbins, 2005;
112
Ranaldi et al., 2009). Lastly, the cue should motivate behavior, and act as a secondary
reinforcer. Conditioned reinforcement assesses the ability of a cue to act as a secondary
reinforcer for drug administration. Rats that sign-track to a reward cue will self-
administer more drug in the presence of that cue (Di Ciano and Everitt, 2004; Everitt and
Robbins, 2005; Robinson and Flagel, 2009; Uslaner et al., 2006). Reinstatement
evaluates the strength of reward learning. After extinction, a cue typically no longer elicits
a learned response. Cues that are strong incentive stimuli will reinstate the learned
behavior when presented after extinction (Saunders and Robinson, 2011).

Androgens are both rewarding and reinforcing (Cooper et al., 2014; Sato et al., 2010), and
impact behaviors that require DA transmission in Acb (Wallin-Miller et al., 2018). Future
studies using Sprague-Dawley rats could test effects of testosterone on PCA, Pavlovian-
to-instrumental transfer, conditioned reinforcement, and reinstatement to evaluate the
potential for drug seeking with AAS abuse. These studies are important given the risk of
human AAS users for dependence (Denham; Kanayama et al., 2009).

113
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Chapter 5. Discussion of experiments and broader significance

5.1 Summary of Findings
Taken together, these studies address the action of androgens in the brain and the
potential consequences for cognition and addiction. All behavioral experiments (Chapters
2 and 4) used high-dose testosterone as a model of anabolic androgenic steroids (AAS)
abuse. The distribution of the androgen receptor (AR) was re-evaluated to determine if
brain areas mediating androgen-sensitive cognitive behaviors express AR (Chapter 3).
Presence of AR could provide a potential mechanism by which androgens and AAS can
influence cognitive behaviors and decision-making.

Chapter 2 evaluated the effects of high-dose testosterone on a novel operant discounting
paradigm for decision making under cognitive effort. This behavioral task required rats
to choose between an easy task to earn a small reward, or a more cognitively-demanding
task to earn a large reward. Both testosterone- and vehicle-treated rats continued to
choose the hard lever associated with the large reward, despite mediocre performance as
the task increased in difficulty. Dopamine (DA) antagonists for D1-like and D2-like
receptors, SCH23390 and eticlopride respectively, had no effect on choice of the hard
lever or task accuracy. Depleting serotonin (5-HT) with 4-chloro-DL- phenylalanine
(PCPA) marginally decreased choice of the hard lever, but significantly decreased task
accuracy. Testosterone-treated rats did not differ significantly from vehicle controls
following either the DA or 5-HT manipulations. Thus, cognitive effort discounting does
not appear to be sensitive to high doses of androgens.

123
Chapter 4 hoped to determine whether high-dose testosterone would alter reward
learning to promote drug seeking on a test of Pavlovian conditioned approach (PCA).
Typically, naïve rats show a range of behavioral responses when trained on PCA, either
preferentially engaging with the cue (lever; sign-tracking) or spending time in the location
of the reward (food cup; goal-tracking). In our study using Long-Evans rats treated with
testosterone or vehicle, we had a high proportion of sign-tracking behavior regardless of
treatment condition. This limited our ability to make conclusions regarding the androgen
sensitivity of PCA.

Chapter 3 utilized tyramide signal amplification (TSA) to enhance staining of the
androgen receptor (AR), allowing for improved visualization in brain regions with low
amounts of AR. TSA-enhanced AR immunostaining revealed a broad distribution of AR
in cognitive brain regions including the prelimbic (PrL) and infralimbic (IL) regions of
the medial prefrontal cortex (mPFC), the orbitofrontal cortex (OFC), and the nucleus
accumbens (Acb) core (AcbC) and shell (AcbSh) in ventral striatum. Improved AR
staining was combined with tract tracing to identify AR-positive afferents to Acb. Few
neurons projecting to Acb were also AR-positive, and those that were co-labeled
originated in CA1 region of ventral hippocampus and the bed nucleus of the stria
terminalis. Although the majority of projections to Acb were not AR-positive, several
brain regions with Acb afferents have numerous AR-positive neurons, including PrL, IL,
OFC in cortex, and the ventral tegmental area (VTA) and substantia nigra pars reticulata
(SNR) in midbrain. The more widespread distribution of AR observed with TSA suggests
androgen sensitivity for a broader range of behaviors, including complex cognitive
behaviors like cognitive effort and PCA.
124
5.2 Androgens, the brain and behavior
Despite the presence of AR in brain regions important for cognitive effort (Chapter 3),
we did not find an effect of AAS on cognitive effort-based decision making (Chapter 2).
AR is present in some brain areas important for cognitive effort (PrL, IL, and AcbC), but
is not present in others including the anterior cingulate cortex (ACC) and basolateral
amygdala (BL; Hosking et al., 2014, 2016; Silveira et al., 2018). Androgen insensitivity
of cognitive effort is surprising, since other discounting tasks using punishment, delay,
probability, and physical effort are sensitive to AAS.

Moreover, both probability and physical effort discounting appear to depend on DA.
Alterations in DA receptor density in AcbSh following high dose-testosterone treatment
likely mediates the sensitivity to reward omission for decision-making on probability
discounting (Wallin et al., 2015; Wallin-Miller et al., 2018). Changes in DA receptors in
AcbC may also account for the willingness to expend more physical effort to earn a large
reward (Donovan et al., 2019). However, cognitive effort discounting is not dependent on
DA, and instead involves 5-HT.

Although 5-HT is necessary for accurate performance on cognitive effort discounting
(Chapter 2), the specific receptors involved remain unknown. AAS treatment in rats
decreases 5-HT levels and alters the density of certain 5-HT receptors (Kindlundh et al.,
2003; Mhillaj et al., 2015). The cognitive effort discounting task is not sensitive enough
to detect subtle changes in the amount of 5-HT in the brain or alterations of 5-HT receptor
expression caused by AAS. Insufficient research has explored the relationship between
changes in 5-HT levels or 5-HT receptors after AAS use and their impact on cognitive
125
behaviors (Silveira et al., 2020). Developing a cognitive task sensitive to alterations in the
5-HT system would prove useful in continuing to investigate the long-term impact of AAS
on serotonergic cognitive functions.

While DA has no effect on cognitive effort, drug-seeking and reward learning do require
Acb DA (Di Chiara et al., 2004); therefore, it is unfortunate that we cannot determine the
effects of AAS on PCA. As discussed in Chapter 4, both strain differences and cue modality
can best account for the high proportion of sign-tracking in our study. Notably, we see AR
in several brain regions associated with PCA, including AcbSh, VTA, OFC, caudate, lateral
septum, and lateral habenula (Flagel et al., 2011). Therefore, making the appropriate
changes by using a different strain of rats (e.g. Sprague Dawley) combined with an
auditory cue in PCA would allow for re-evaluation of potential AAS effects. With these
modifications, I would expect to find some influence of AAS on the development of drug-
seeking behavior. The substantial overlap of AR in brain regions important for PCA, and
the dependence on DA for reward-learning greatly suggests that these behaviors should
have some androgen sensitivity. Further, my study only examined one domain within
drug-seeking behavior. As discussed at the end of Chapter 4, future studies could evaluate
the effects of AAS on drug-seeking and reward-learning using several different behavioral
tasks, including Pavlovian-to-instrumental transfer, conditioned reinforcement, and
reinstatement.

An important question in addiction research concerns identifying the lasting changes in
synaptic plasticity following drug cessation. Some of these long-term modifications likely
contribute to the ongoing problems of renewed drug craving and drug-seeking that lead
126
to relapse. Of greatest concern is whether these changes in the brain are permanent.
∆FosB is a transcription factor that accumulates in Acb and dorsal striatum after repeated
exposure to drugs of abuse and is implicated in addiction and compulsive behaviors
(Damez-Werno et al., 2012; Nestler, 2001). Unlike other Fos-family transcription factors
which have a short half-life, ∆Fos-B is present months after drug cessation (Robison and
Nestler, 2011; Ruffle, 2014). Thus, it has been proposed as a “molecular switch for
addiction” (Nestler, 2001; Nestler et al., 2001), expressed in D1-type medium spiny
neurons of Acb (Ruffle, 2014). Interaction between androgens and ∆FosB expression in
Acb seems likely, given both the presence of AR and androgen sensitivity of neurons
expressing D1-like receptors in Acb. Measuring ∆FosB in Acb by western blot may
determine if there is an increase in expression similar to other drugs of abuse. This would
clarify our understanding of how AAS modify neurocircuitry in addiction and produce
lasting changes.
Although there was no effect of AAS on cognitive effort discounting, and we were unable
to determine an effect on PCA, the distribution of AR we observe in Chapter 3
complements the existing literature regarding cognitive behaviors affected by AAS
(Tobiansky et al., 2018). Several of these behaviors, like cognitive flexibility (Wallin and
Wood, 2015) and decision-making (Wallin-Miller et al., 2018), depend on DA. Acb DA is
specifically important for probability and physical effort operant discounting, and
behavioral tests of drug reward and reinforcement. More so, the density of DA receptor
subtypes following AAS treatment changes in AcbC and AcbSh (Kindlundh et al., 2001).
The direct mechanism by which androgens alter DA receptors in Acb remains unknown.
127
Direct activation of AR in Acb may be a potential mechanism by which androgens
modulate DA receptors. When bound to its ligand, AR functions like a transcription
factor, leading to the up-regulation or down-regulation of specific genes. Identifying
differences in gene expression in Acb is possible with RNA sequencing (RNAseq). RNAseq
is a powerful tool for exploring changes and identifying candidate genes (Gegenhuber and
Tollkuhn, 2020; Shay et al., 2020). Acb-specific differentially expressed genes identified
with RNAseq following high dose testosterone treatment could reveal candidate genes
related to DA receptor trafficking and metabolism. Investigating the specific genomic
effects of AR activation in Acb could provide valuable insight for understanding these
structural changes and the underlying mechanisms.

5.3 Complexities of AAS recreational drug use, abuse, and addiction
Androgens and other sex hormones in the brain have similarities to 5-HT in their
complexity. Like 5-HT, they act as neuromodulators, not directly causing specific
behaviors, but biasing behaviors to occur. Viewing androgens as neuromodulators may
help clarify research regarding the side effects of AAS on mood. A consequence of
androgens behaving like neuromodulators is that treatment of long-term abuse and
addiction in human users may be more difficult relative to other drugs of abuse.

Although the number of known sex hormone receptors is few in comparison to 14
members of the 5-HT receptor family* (Palacios, 2016), androgens can be aromatized to

*
“Some of these receptors presented [in] different molecular forms, reaching a number that we
would [not have] dream[ed] of, even in our more lysergic dreams [sic]” (Palacios 2016).
128
estrogen in the brain, thereby acting via both AR and estrogen receptors. What’s more,
androgens can act as allosteric modulators of GABAA receptors, effecting GABAergic
transmission (Clark et al., 2006; Henderson et al., 2006). Better identification of the
molecular targets of AAS could help reveal the direct mechanism by which androgens
alter DA receptors in Acb.

The lack of negative effects of AAS on cognition in Chapters 2 and 4 does not mean AAS
are safe. Combined reports from human and animal studies indicate that the side effects
of AAS use for physiological and psychological health are far from benign. Yet in
comparison to other drugs of abuse, AAS do not have profound effects on cognition and
decision-making. However, the cognitive deficits, and learning and memory impairments
associated with alcohol, stimulants, and opioids may be exacerbated by combined AAS
abuse. In addition, multiple-drug use in AAS users may worsen problems with physical
dependency and relapse. Notably, AAS can increase susceptibility to an opioid drug
overdose, even at low levels of opioid drug intoxication (Thiblin et al., 2000). How little
we know about the long-term consequences of AAS abuse on brain structure and function
is troubling, given that co-abuse with opioids is quite common.

Studies evaluating the impact of early and ongoing AAS exposure during adolescence are
necessary to fully understand the long-term influence on brain maturation, structure, and
function. We know that opioids have long-term effects on brain structure that persist after
cessation of use; opioid use beginning earlier in life exacerbates these effects. Since AAS
have similarities with opioids, we would expect them to have lasting effects on the brain.
Not enough is known regarding selective vulnerability of the brain to AAS exposure in
129
either humans or rodents. Most users begin taking AAS in their mid-teens to early
twenties, while the brain is still developing. During this period, the brain undergoes
significant plastic changes and maturation. Early onset of AAS use can have lasting
effects, especially on reward circuitry and executive functions. It remains unknown
whether AAS may produce long-term structural changes that could have profound
consequences for behavior and cognition later in life.

Furthermore, the observed effects of AAS on DA-dependent behaviors in Acb may
produce structural changes in the reward pathway that could exacerbate the negative
effects of other substances, including cognitive deficits. This is concerning since many
AAS users abuse multiple drugs, like alcohol and opioids. This could lead to greater
difficulties treating individuals with substance abuse and dependency issues.

Treating substance abuse and dependency issues in AAS users may be especially difficult
given the high concordance with other mental disorders. Evolving opinions are now
conceptualizing AAS use in some individuals as a symptom of an underlying body image
disorder, specifically muscle dysmorphia (Goldman et al., 2019; Hildebrandt et al., 2010;
Hildebrandt et al., 2018; Piacentino et al., 2015; Pope et al., 2017). Body dysmorphic
disorder is a type of obsessive-compulsive disorder in which the individual is preoccupied
with the obsessive idea that their body or appearance is severely defective, therefore the
individual is driven to use exceptional means to correct these perceived flaws. Eating
disorders, like anorexia or bulimia nervosa, may also be present. Treating individuals with
body dysmorphic and eating disorders is incredibly challenging because of the high rates
of relapse, similar to drugs of abuse. Healthy lifestyle habits are encouraged during drug
130
treatment and recovery, with an emphasis on exercise as a positive coping mechanism.
Avoiding potential drug cues is also imperative. However, in former AAS users, working-
out and frequenting the gym presents numerous potential drug cues that can trigger
cravings and relapse. Determining whether AAS exacerbate these issues can be explored
further with PCA in rats to identify context-drug associations. Greater recognition of the
complexities and the difficulty of drug rehabilitation and mental health treatment in AAS
users is imperative for long-term success and drug abstinence.

Ultimately, increasing public health awareness is necessary to change the mindset
regarding AAS. It is important to emphasize drug-free approaches to health and fitness
to curtail the image of AAS as benign supplements. Instead, AAS should be perceived as
shortcuts to muscularity and strength that are not part of an active and healthy lifestyle.
High schools, colleges, universities, gymnasiums, and community recreational centers are
ideal environments to disseminate informational posters and educational programs
about the consequences and concerns of AAS use. One harm-reduction initiative could
include the introduction of a drug counselor on staff at fitness centers, offering free
consultations for those at-risk. These counselors could also assist by referring individuals
to treatment programs when recovery is desired and connecting them with support
groups while maintaining abstinence. Additionally, many AAS users do not report their
recreational use to health care providers, and often may appear ‘healthy’ given their
interest in sports, fitness, and exercise. Continuing educational programs for doctors
could help address the challenges in recognizing those at risk and how to provide
adequate levels of treatment and care. Doctors could also strive to create an environment
where patients feel safe discussing their drug use.
131

5.4 Conclusion
While it is important to emphasize the dangers of AAS, it is necessary to not dehumanize
those suffering from addiction. Stigmatizing the user instead of the drug prevents those
struggling from getting the help they need. Only through increased awareness and
education can the community as whole reduce the public health risks of AAS. Our best
hope as scientists is to understand the complexities of the brain enough to assist
individuals and communities in this endeavor.

132
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Abstract (if available)

Abstract Anabolic-androgenic steroids (AAS) are drugs of abuse, taken to increase muscle mass and athletic performance, but with negative effects on physiology and behavior. Similar to other drugs of abuse, AAS users are at risk for developing maladaptive behaviors, including impaired decision-making. Rats treated with high-dose testosterone, a model of AAS abuse, show altered decision-making under physical effort. Since modern society places greater emphasis on cognitive rather than physical effort, Chapter 2 evaluates the effects of AAS on a novel operant discounting paradigm for decision-making under cognitive effort. Cognitive effort operant discounting is not sensitive to high-dose testosterone and does not require dopamine receptor activity. Because all AAS are synthetic derivatives of testosterone, they can act via androgenic mechanisms in the brain. Specifically, AAS can bind to the androgen receptor (AR) to influence behavior. AAS affect dopamine-dependent behaviors mediated by the mesocorticolimbic pathway

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Creator Dokovna, Lisa Beth (author)

Core Title Addicted to androgens: consequences for cognition and behavior

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Publication Date 05/12/2020

Defense Date 03/20/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

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