1

NEUROFUNCTIONAL DEFICITS DURING DECISION-MAKING AND

AFFECTIVE PROCESSING IN CHRONIC MARIJUANA USERS

BY

MICHAEL J. WESLEY

A Dissertation Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

in Partial Fulfillment of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

in Physiology and Pharmacology

December 2010

Winston-Salem, North Carolina

Approved By:

Linda J. Porrino, Ph.D., Advisor

Examining Committee:

Robert C. Coghill, Ph.D., Chairman

Sam A. Deadwyler, Ph.D.

Robert E. Hampson, Ph.D.

Anthony Liguori, Ph. D.

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ACKNOWLEDGEMENTS

First, I would like to thank Linda Porrino. Before I entered Linda’s lab she said to

me, “I am going to make a human researcher out of you”. I am incredibly grateful that

Linda followed through on this generous endeavor. She took me into her lab and has

been a sensational mentor. Time and again she has captivated me with her unique

insights into problems, professional and personal, that were overwhelming for me. Linda

has a unique gift for knowing how to sharpen any instrument. I am thankful for the time

and patience she put into my development as a scientist and will always consider her

mentorship a gift. I would also like to thank other members of the Porrino Lab who have

helped me along the way: Mack Miller for his programming expertise and help with data

processing and Marla Torrence for her superb ability to recruit and test participants.

Many thanks to Hilary Smith, Tom Beveridge, Erin Shannon, Katy Lack and Katie Gill

for insightful comments at various lab meetings, whether in pirate garb or not, that helped

me understand and communicate my research ideas and results. A very special thank you

goes out to Colleen Hanlon, who entered the lab as a postdoctoral fellow when I started

as a graduate student. Her passion and drive for science immediately rubbed off on me

and fueled my own desire for knowledge like never before. Colleen took the time to

teach me, and learn with me, the various aspects of EVERYTHING. She has

continuously been a one-of-a-kind teacher and friend. The thankfulness I feel for Colleen

is truly ineffable.

I’d like to thank members of my committee: Bob Coghill for advice regarding

imaging analysis and career development, Sam Deadwyler for conversations about the

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cannabinoid system that helped me see pictures so big that my head nearly exploded, Rob

Hampson for always expressing interest in my research and for encouraging me to attend

scientific meetings and give talks, and Tony Liguori for his insights into conducting

research in humans and for his help with various statistical analyses. I much appreciate

the time and effort each member has put into my development.

Finally, this manuscript is dedicated to my family and friends for their love and

support. In particular, thank you to Frankie Gordon for her encouragement and support

throughout all of my educational endeavors. A special thanks to my wife Jill Wesley for

her encouragement and support during my graduate career. To my older brother Ken,

who has always been and will always be my personal hero and who for some reason

thinks that I am smarter than him (I am not), many thanks brother. To my older sister

Cela, who was the first person to show me that regardless of how difficult things are you

can make them better with effort, you are a gift to anyone lucky enough to know you sis.

At last, to my grandmother Annie Lee Cunningham who has always encouraged me to

follow my dreams and who has never run out of prayers for me, I would not be here

without you. I owe the best parts of who I am to all of you, thank you.

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TABLE OF CONTENTS

Page

LIST OF FIGURES……………………………………………………………….............v

LIST OF TABLES……………………………………………………………………….vii

LIST OF ABBREVIATIONS……………………………………………………………ix

ABSTRACT……………………………………………………………………………….x

CHAPTER

I. INTRODUCTION………………………………………………………………..1

II. ALTERED FUNCTIONAL ACTIVITY IN CHRONIC

MARIJUANA USERS DURING SPECIFIC COMPONENTS

OF DECISION-MAKING……………………………………………………….41

III. POOR DECISION-MAKING BY CHRONIC MARIJUANA

USERS IS ASSOCIATED WITH DECREASED FUNCTIONAL

RESPONSIVENESS TO NEGATIVE CONSEQUENCES…………………….76

IV. ALTERATIONS IN FUNCTIONAL PROCESSING OF STIMULI

JUDGED TO BE EMOTIONAL IN CHRONIC MARIJUANA

USERS………………………………………………………………………….116

V. DISSCUSSION…………………………………………………………………150

CURRICULUM VITA…………………………………………………………………169

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LIST OF FIGURES

CHAPTER II

Figure 1: Iowa gambling task (IGT) event types…………………………………...48

Figure 2: Behavioral performance on the IGT:

Controls versus chronic marijuana users (MJ Users)……………………55

Figure 3: Brain activity during the selection component of the IGT………………58

Figure 4: Brain activity during the evaluation component of the IGT……………..62

CHAPTER III

Figure 1: Behavioral Performance in three sections of the

Iowa Gambling Task: Controls versus MJ Users………………………..93

Figure 2: The difference in functional activity of Controls

and MJ Users during all evaluation events (wins + losses)

in the strategy development phase of the IGT…………………………...95

Figure 3: The difference in functional activity of Controls

and MJ Users during monetary loss evaluation in

the strategy development phase of the IGT……………………………...96

Figure 4: Improvement in IGT performance correlated with

the functional brain response to loss evaluation

during strategy development in Controls and MJ Users…………………99

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CHAPTER IV

Figure 1: Distribution of the number of IAPS stimuli judged

as having positive emotional content, little to no

emotional content, or negative emotional content

in Controls and MJ Users……………………………………………….131

Figure 2: Brain activity in Controls and MJ Users independently

while viewing stimuli judged as having emotional content,

compared to stimuli judged as having little to no

emotional content……………………………………………………….133

Figure 3: Direct comparisons of brain activity between Controls

and MJ Users while viewing stimuli judged as having

emotional content, compared to stimuli judged as having

little to no emotional content…………………………………………...134

Figure 4: Magnitude of functional responses while determining the

emotional content of visual stimuli, regardless of valence,

in Controls and MJ Users……………………………………………….135

Figure 5: Magnitude of functional responses to stimuli considered

to be positive (POS), negative (NEG), or contain little

to no emotional value (NEU) in the amygdala, insula and

medial prefrontal cortex and anterior cingulate cortex

(mPFC) in Controls and MJ Users………..………………………….....137

CHAPTER V

Figure 1: Decreased function in the medial prefrontal cortex

and anterior cingulate cortex of MJ Users to affective

stimuli during the IGT task (complex decision-making)

and the IAPS task (simple emotional judgments)………………………158

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LIST OF TABLES

CHAPTER II

Table 1: Group demographics for the IGT whole task analysis…………………...54

Table 2: Clusters of significant BOLD activity during all IGT

selection events in Controls and MJ Users independently,

and directly compared between groups…………………………………..59

Table 3: Clusters of significant BOLD activity during advantageous

and disadvantageous selections on the IGT between

Controls and MJ Users…………………………………………………...59

Table 4: Clusters of significant BOLD activity during all IGT

valuation events in Controls and MJ Users independently,

and directly compared between the groups………………………………63

Table 5: Clusters of significant BOLD activity during win and

loss evaluation on the IGT between Controls and MJ Users…………….64

CHAPTER III

Table 1: Group demographics for the IGT strategy development

phase analysis…………………………………………………………….91

Table 2: Clusters of significant differences in BOLD signals

during Win and Loss evaluation between Controls

and MJ Users during strategy development on the IGT…………………97

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CHAPTER IV

Table 1: Group demographics for the IAPS task analysis……………………….130

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LIST OF ABBREVIATIONS

Δ9-THC Δ-9-tetrahydrocannabinol

ACC Anterior Cingulate Cortex

BOLD blood oxygen level dependent

CB1 central nervous system cannabinoid 1 receptor

fMRI functional magnetic resonance imaging

IAPS International Affective Picture System

IGT Iowa Gambling Task

MJ Users long-term chronic marijuana users

MNI Montreal Neurological Institute

mPFC medial prefrontal cortex

PET positron emission tomography

ROI(s) region(s) of interest

SPM statistical parametric maps

THC Δ9-tetrahydrocannabinol

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ABSTRACT

Wesley, Michael J.

NEUROFUNCTIONAL DEFICITS DURING DECISION-MAKING AND

AFFECTIVE PROCESSING IN CHRONIC MARIJUANA USERS

Dissertation under the direction of

Linda J. Porrino, Ph.D., Professor and Chair of Physiology and Pharmacology

Marijuana is the most frequently used illegal drug in the country with medicinal

and recreational use increasing. The present series of studies was designed to examine

functional brain activity associated with altered decision-making and affective or

emotional processing in long-term chronic marijuana users (MJ Users).

The first aim was to determine how activity in MJ Users is altered during different

components of the decision-making process. As MJ Users performed poorly on the Iowa

Gambling Task (IGT), they had greater activity during selection and decreased activity in

response to feedback, compared to non-marijuana using controls (Controls), suggesting

that MJ Users are more engaged while implementing choices but functionally insensitive

to the consequences of those choices during decision-making.

To understand how functional insensitivity to feedback related to the development

of problem solving strategies, a series of analyses were conducted during the earliest

phase of the IGT. MJ Users were found to have decreased functional responses to

aversive feedback, and they lacked activity in the medial prefrontal cortex (mPFC) and

anterior cingulate cortex (ACC) that predicted learning in Controls. These data

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demonstrated that insensitivity to aversive feedback was directly related to the inability of

MJ Users to develop successful performance and suggest that MJ Users have failed

integration of aversive feedback information into executive functioning processes.

A final series of analyses were conducted to determine if affective processing was

altered in MJ Users for stimuli considered to be emotional. MJ Users and Controls

judged the same stimuli as emotional; however, MJ Users exhibited decreased functional

responses in the mPFC and ACC while viewing emotional stimuli. Response magnitudes

for positive were significantly less in MJ Users in several brain areas and hypoactive in

the mPFC and ACC, suggesting a more cognitive, rather than emotional, functional

response to affective stimuli.

Taken together, these studies are important because they identify a potentially

serious side effect of long-term marijuana use. They show that MJ Users have decreased

functional responses in brain areas crucial for cognitive and emotional processing and

suggest that long term marijuana use leads to compromised decision-making abilities and

experience of emotions.

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CHAPTER I

INTRODUCTION

Cannabis Epidemiology

Cannabis (Cannabis Sativa) is a plant that for centuries has been used for various

reasons, including agricultural, industrial, medicinal, and recreational purposes (Block et

al. 2002; Hindmarch 1972). The flowers of the cannabis plant, marijuana, contain

cannabinoid compounds that have the ability to alter brain function and produce a

multitude of effects. In the United States, federal penalties and enforcement provisions

against cannabis were established with the passing of the 1937 Marijuana Tax Act.

Notwithstanding, marijuana remains the most widely used illicit drug in the United States

and its use is increasing (Johnston et al. 2009a). In recent years lobbying for its

medicinal benefits has led to medicinal use laws in fourteen of the fifty states, including

the District of Columbia. This medicinal use, however, occurs without regard to federal

standards that would otherwise establish dosing parameters and, more importantly, side

effect profiles associated with marijuana’s use as a medicine. In many cases

consumption is determined by the individual patient, and therefore misuse is likely to

occur (Dresser 2009; Seamon 2006). Poor regulation of marijuana as a medicinal drug

has also lead to increased use by individuals without debilitating diseases (Aggarwal et

al. 2009).

Recreational marijuana use has also increased in recent years (Licata et al. 2005).

Based on 2002, 2003 and 2004 surveys, it was reported that nearly 10.71% (25.8 million)

of Americans age 12 or older abused marijuana within the past year, with 6.12% abusing

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the drug within the past month (National Survey on Drug Use and Health). It is

estimated that around 6 thousand Americans initiate marijuana use each day with 62.2%

of those individuals being younger than 18 years old. In school attending children, the

proportion of 8th

-graders who perceive smoking marijuana as harmful and disagree with

its use has decreased, while the percent of 12th

-graders using marijuana on a daily basis

increased from 4.6% to 5.4% between 1995 and 2008 (Johnston et al. 2009b). There

have also been observed increases in the potency of the major psychoactive ingredient

found in marijuana as well as user preference to consume high potency marijuana (Chait

and Burke 1994; Licata et al. 2005). Consequently, the number of individuals entering

drug treatment facilities reporting marijuana as their major problem drug has also

increased (Compton et al. 2004).

Cannabinoids and the Brain

The endocannabinoid system, CB1, and marijuana. Marijuana yields the

majority of its effects by influencing function of the endogenous cannabinoid

(endocannabinoid) system. The major receptor for this system in the central nervous

system (CNS) is the cannabinoid 1 receptor, CB1. These receptors are G-protein coupled

to the Gi/o family of second-messenger proteins (Howlett 1984). When a cannabinoid

agonist binds to CB1 in the brain, it results in many intracellular effects including the

inhibition of adenylate cyclase (Howlett et al. 1986), decreased in calcium conductance

(Caulfield and Brown 1992), and increased potassium conductance (Childers et al. 1993).

There are two main endocannabinoid agonists in the brain, N-arachidonoyl ethanolamine

(anandamide) and 2-arachidonoyl glycerol (2-AG), and their actions are terminated by

fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase. Unlike typical

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neurotransmitter messengers, endocannabinoids are retrograde messengers released from

depolarized postsynaptic terminals to mainly influence activity at presynaptic terminals

(Hofmann et al. 2006; Maejima et al. 2001).

Marijuana contains many different (>65) cannabinoid compounds, of which Δ-9-

tetrahydrocannabinol (Δ9-THC) is the major psychoactive ingredient (Mechoulam et al.

1967; Mechoulam and Gaoni 1967). Δ9-THC is a partial CB1 agonist and has been

shown to modulate both of the major inhibitory and excitatory neurotransmitter systems

in the brain, GABA (Katona et al. 2001) and Glutamate (Auclair et al. 2000),

respectively, as well as more specific systems such as the striatal dopaminergic “reward”

system (Bossong et al. 2009). CB1 agonists, such as Δ9-THC, can alter basic

physiological processes leading to many effects, such as increasing pain thresholds (Azad

et al. 2005). CB1 agonists have also been shown to decrease learning and memory

abilities (Carlson et al. 2002; Chevaleyre and Castillo 2004; Deadwyler et al. 2007;

Lichtman et al. 2002; Moreira and Lutz 2008) as well as diminish stress, anxiety, and

emotional responsiveness (Azad et al. 2004; Carrier et al. 2005; Chhatwal et al. 2005;

Marsicano et al. 2002; Ribeiro et al. 2005; Wotjak 2005).

CB1 distribution in the brain. Marijuana produces many diverse effects

throughout the brain largely depend on the cell type and the brain location of the CB1

receptor. To date, CB1 is known as one of the most abundant G-protein coupled

receptors in the brain, and it has a largely ubiquitous distribution. CB1 receptors are

highly conserved across phylogeny and analogous distribution patterns exist in the brains

of rodents (Herkenham et al. 1991), primates (Eggan and Lewis 2007) and humans (Glass

et al. 1997; Wong et al. 2010). The highest reported concentrations of CB1 are in the

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cerebellum, the outflow nuclei of the basal ganglia, the substantia nigra pars reticulate,

the globus pallidus as well as the hippocampus and amygdala (Ashton et al. 2006;

Herkenham et al. 1991; Herkenham et al. 1990; Katona et al. 2001; Kawamura et al.

2006; Liu et al. 2003; McDonald and Mascagni 2001). In primates and humans, high

concentrations also exist throughout neocortical structures (Hamill et al. 2009; Wong et

al. 2010). It is the presence of CB1 receptors in brain areas known to be functionally

involved in basic cognitive (e.g. hippocampus) and emotional (e.g. amygdala) processing,

as well as neocortical areas that integrate these processes (e.g. medial frontal cortex and

anterior cingulate cortex), that allows cannabinoids to effect multiple aspects of

performance and perception.

Behavioral Effects of Marijuana

Acute effects of marijuana. Acute marijuana effects include impaired

psychomotor performance, memory, cognitive abilities and affective states (Lane et al.

2005a; Lane et al. 2004; Lane et al. 2005b; McDonald et al. 2003; O'Leary et al. 2002;

Rossi et al. 1978; Vadhan et al. 2007). For example, differences in simulated driving

abilities have been reported for individuals smoking marijuana with low (1.77%) versus

high (3.95%) concentrations of Δ9-THC (Liguori et al. 1998). Compared to a low dose, a

high dose of Δ9-THC increased measurements of body sway while balancing and brake

latency while driving. It has also been shown that infrequent marijuana users who

received oral Δ9-THC (15 mg) performed poorly on episodic memory and learning tasks

(Curran et al. 2002). Other studies have shown that high doses of smoked Δ9-THC

impair delay-dependent discrimination, a measure of working memory (Lane et al.

2005a). Oral Δ9-THC (15 mg) has also been shown to increase impulsive responding on

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a stop task but not a go/no-go task (McDonald et al. 2003), suggesting that while Δ9-THC

may alter some aspects of impulsivity, other aspects may be preserved.

Acute marijuana use has also been shown to relieve symptoms of stress and

anxiety (Bonn-Miller and Moos 2009; Buckner et al. 2007; Buckner and Schmidt 2008).

Anxiety symptoms have also been shown to predict relapse to marijuana use (Bonn-

Miller and Moos 2009). Early studies observed the ability of marijuana to alter an

individual’s moods as well as one’s perception of the moods of other individuals. For

example, while receiving controlled marijuana for one month, the average mood scores of

intoxicated individuals changed to match the mood ratings of individuals in their

immediate environment, suggesting that marijuana increases a person’s susceptibility to

the moods of others (Rossi et al. 1978). A study the next year, however, demonstrated

that marijuana (Δ9-THC = 6mg) decreased the ability of an individual to correctly

perceive the emotional state of other individuals (Clopton et al. 1979). Together, these

data demonstrate that acute marijuana use has the ability to impair basic cognitive

processes as well as alter affective states, such as mood, stress and anxiety.

Long-term effects of marijuana. Long-term chronic marijuana use has been

associated with decreased attention and memory function, impaired perception and

decreased executive functioning abilities (Gruber et al. 2009; Solowij et al. 1991; Solowij

et al. 2002; Whitlow et al. 2004). For example, long-term chronic marijuana users (MJ

Users) have been shown to perform poorly on complex auditory tasks that require

selective attention (Solowij et al. 1991). MJ Users have also been found to have poor

performance on complex decision-making tasks such as the Iowa Gambling Task (IGT;

Hermann et al. 2009; Whitlow et al. 2004), compared to non-marijuana using controls

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(Controls). This task is considered to have “real world” relevance and relies on the

processing of positive and negative stimuli to achieve a long-term goal. To perform this

task, participants select cards randomly from four card decks under ambiguous

conditions. Each selection produces a monetary gain or win; however, some selections

also result in a monetary loss. Over time, based on the wins and losses associated with

each deck, two of the decks emerge as advantageous and two disadvantageous. The

disadvantageous decks produce larger immediate gains but larger losses over time while

the advantageous decks yield smaller immediate gains but smaller losses over time.

Participants must process the win and loss contingencies associated with there choices

and learn that advantageous performance involves rejecting the opportunity for large

immediate gains that produce larger losses over time. Since decision-making is a

complex process that involves independent and integrative systems, the source of this

deficit in MJ Users is not straightforward. One of the major goals of this dissertation,

therefore, is to use the IGT to isolate brain activity during specific components of

the decision-making process to identify the source of decision-making deficits in MJ

Users.

Heavy marijuana use has also been associated with altered mood, anxiety and

depression. Though there is not conclusive evidence that heavy marijuana use causes

changes in these domains, studies demonstrate that there is a relationship between heavy

use and affective processing. For example, it is three times more likely that an individual

with marijuana dependence meets criteria for affective disorders, compared to non-users

(Degenhardt et al. 2001). A metaanalysis of longitudinal studies reported that heavy

marijuana use may increase depressive symptoms among some users (Degenhardt et al.

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2003). More recently, it was demonstrated that the risk of anxiety and mood disorders is

greater in MJ Users, compared to light users and Controls (Cheung et al. 2010). Altered

mood states are also observed following the cessation of heavy marijuana use. This

marijuana withdrawal syndrome (Budney and Hughes 2006), includes increases in

anxiety and aggression scores seen a couple of days following the cessation of use that

can persist for a couple of weeks across abstinence. Understanding that long-term

marijuana use is associated with altered affective states, another goal of this

dissertation is to examine the neurofunctional processing of affective information in

MJ Users, both in the context of complex decision-making and while making simple

emotional judgments.

From behavioral data, it is clear that acute and long-term marijuana use is

associated with changes in cognitive and affective domains. These observations have led

scientists to use neuroimaging techniques, similar to the techniques used for this

dissertation, in an attempt to identify the alterations in brain structure and function that

might account for these deficits. Imaging studies have been performed in individuals

receiving cannabinoid agonists as well as in MJ Users and have begun to identify how

brain structure and function is altered in marijuana users.

Measuring Brain Structure and Function

There are many techniques used to measure structural (e.g. size and density) and

functional (e.g. energy usage) information about the brain. Each technique relies on the

quantification of data contained within voxels. In neuroimaging, voxels are small three

dimensional units, often millimeters in size, whose values represent structural or

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functional information about a particular place in the brain. The recreation, manipulation

and analysis of these values, collected through various techniques (e.g. MRI, fMRI,

PET), provide the scientific basis for understanding differences between groups of

individuals. Data from voxels are often combined with behavioral data, such as

performance on a task, to reveal relationships between outwardly quantified behavior and

underlying structure and function. In the following section, a brief overview is given for

commonly used neuroimaging techniques that have been used to study brain structure and

function in marijuana users.

Measuring Structure. Magnetic Resonance Imaging (MRI) is an imaging

technique commonly used to visualize structures within the body. MRI provides high

resolution contrast between different tissues types. The technique works by placing

individuals in a powerful and stable magnetic field which aligns hydrogen atoms in an

organized and predictable, relaxed position. Radio frequency pulses are then applied to

this global field in a systematic manner in order to disturb, or flip the molecules out of

alignment and into a transverse state. As the molecules relax or move back into

alignment with the global magnetic field, they produce detectable rotating spins that can

be measured by the MRI scanner. Depending on various tissue properties (e.g. density)

where the molecules are located and the pulse sequences used to disrupt the molecules,

they relax back to the global field at different rates. This results in different intensity

values within the brain. When these values are converted into a linear grayscale in each

voxel, they produce observable contrasts between different tissue types (e.g. grey and

white matter). The visualization and quantification of these contrasts allow for the

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measurement of specific brain structures and comparison of structural features between

groups.

Another method commonly used to identify structural differences is voxel-based

morphometry (VBM). VBM relies on an analysis approach called statistical parametric

mapping (SPM) where fitting or moving brain data (structural or functional) into a

common 3-D space (template) corrects individual differences in anatomy. This process is

typically referred to as normalizing the data. Normalizing allows comparisons to be

made between individuals and groups on a voxel by voxel basis. As part of the

normalizing process, a 3-D matrix is created for each person that represents the amount

of movement necessary to place each voxel into the common template space. In VBM,

these movement maps are analyzed to reveal altered structure size between groups. For

example, if voxels corresponding to a particular brain structure must be moved more in

one group, compared to another, in order to fit into standard template space, then this is

evidence of altered structure.

Diffusion Tensor Imaging (DTI) is a relatively new method used to examine

structural differences between groups. DTI measures how water molecules flow in 3-D

and is considered an indirect measure of structural integrity. DTI data is collected in the

MRI scanner by pulsing the brain with frequencies that produce movement of water in

specific directions. After pulsing in numerous directions, values are calculated that

characterize how the water was “allowed” to flow in each voxel. The two main values

calculated from DTI are fractional anisotropy (FA) and mean diffusivity (MD). FA

measures the primary direction that water flowed within a voxel, and MD measures how

much water was present to flow in a voxel. This technique is most commonly used to

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measure the integrity of myelinated axons or white matter within the brain. This is due to

the fact white matter is a fatty tissue and is more hydrophobic (i.e. repels water more)

than grey matter. White matter thus restricts the flow of water to a single primary

direction coincident with the parallel space between myelinated axons. Highly restricted

or directional water flow results in a larger FA value compared to water that is allowed to

flow equally in all directions, which produces a smaller FA value. When low FA values

are observed in voxels corresponding to white matter this is interpreted as decreased

white matter integrity, as these water molecules are not being restricted to a primary

direction by healthy myelin.

Measuring Function. Within the last twenty years, there have been a few

different techniques used to measure brain function in MJ Users and Controls. Some of

these techniques are more invasive than others, requiring the injection of radioisotopes in

order to calculate and visualize function.

Many of the first neuroimaging studies performed in marijuana users utilized

single photon emission computed tomography (SPECT) to calculate function. This

technique is relatively inexpensive, compared to other neuroimaging techniques and

requires the injection of a gamma-emitting radioisotope (i.e. tracer) into the bloodstream.

This radioisotope is usually attached to a ligand that is tissue or protein specific. When

the ligand binds to a target in the brain, gamma radiation is emitted and directly measured

by a gamma camera. This process is very similar to Positron Emission Tomography

(PET). However, in PET imaging, when an injected radioisotope bound ligand binds to

its target, positrons are emitted that annihilate with electrons within a few millimeters.

This reaction results in the emission of two gamma photons in opposing directions. The

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PET scanner detects these coincident events which provide more localization information

and resolution compared to SPECT. In PET imaging, when fluorodeoxyglucose (FDG),

an analog of glucose, is the radiotracer injected, the concentration of activity calculated

represents metabolic activity in terms of regional glucose uptake. In PET studies, the

radiotracer is typically injected followed by the performance of a task outside of the

scanner. The tracer is taken up throughout the brain during task performance. After the

task is complete, participants are put into the scanner to see which brain areas utilized

glucose (i.e. were at work) during the task. A limitation to this technique is that

functional activity is not observed during the task itself and therefore data can not be

isolated which represent brain activity during specific portions of task performance.

The least invasive and most commonly used technique to measure functional

brain activity today is functional Magnetic Resonance Imaging (fMRI). This technique is

noninvasive, widely available, and is used to measure resting-state brain activity (activity

observed when the brain is not being challenged to perform a task) as well as activity

during the performance of various behavioral tasks. This technique measures the

hemodynamic response (change in blood flow) related to neuronal activity. The signal

derived from fMRI is the blood-oxygen level dependent (BOLD) signal. The basis of

this signal is cellular activity. Like all cells in the body, neurons require energy to work.

Energy is needed for cells to work and it is supplied to cells in the form of oxygen in the

blood. This process is dynamically regulated so that active cell populations receive more

oxygen, and inactive populations receive less oxygen. Each hemoglobin molecule within

the blood contains a magnetic iron heme and carries up to four oxygen molecules at a

time to cells. As hemoglobin releases large amounts of oxygen to active neuronal

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populations, an observable change in the magnetic properties of hemoglobin can be

detected. The BOLD signal is proportional to the level of work being performed by

neuronal populations and has been correlated with population activity and local field

potentials within specific brain areas (Logothetis 2003).

Event-related fMRI is the specific imaging technique used in the studies

conducted for this dissertation. In event-related fMRI, the BOLD signal is recorded as

participants perform a task that has been designed to isolate hemodynamic responses to

specific events within the task being performed. This technique has the distinct

advantage of being able to compare functional activity to specific events within a task,

both between and within groups.

Complex Decision-making and Brain Function. As previously described, the IGT is a

task used to examine complex decision-making in humans. Since decision-making is a

multifaceted process, researchers have used neuroimaging techniques in combination

with the IGT to better understand the neural correlates of the decision-making process.

For example, activity has been isolated during the selection component of the IGT, when

participants select cards randomly from one of the four card decks. The selection

component has been further divided into advantageous or disadvantageous selections.

Activity has also been isolated during the feedback or evaluation component of the IGT,

when information about the consequences of choices is revealed. Feedback can be

further divided into positive (monetary gains) or negative (monetary losses) feedback

events.

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One study examining brain activity during specific components of the IGT

revealed that the anticipation or selection component of the task elicited activity in the

insula and the striatum while the outcome or feedback component induced activity in the

inferior parietal cortex (Lin et al. 2008). This same study observed the largest negative

events (monetary losses) during feedback evoked activity in the medial prefrontal cortex

(Lin et al. 2008). This observation is particularly interesting given that the monetary

losses on the IGT provide the necessary information to perform well on the task over

time and that IGT was originally developed to examine executive functioning deficits in

patients with lesions in the medial prefrontal cortex (Bechara et al. 1994). Another

imaging study focused on prefrontal cortical function during the IGT. In this study,

activity in the ventral medial prefrontal was observed during decision-making (Lawrence

et al. 2009). This study also demonstrated that selections made on disadvantageous

decks, compared to advantageous, induced activity in the medial prefrontal cortex, lateral

orbitofrontal cortex, and insula. Finally, Striato-thalamic regions responded to wins more

than losses (Lawrence et al. 2009). This study concluded that deciding advantageously

under initially ambiguous conditions may involve the ventral and dorsal prefrontal

cortices.

Neuroimaging Studies of Cannabinoids

Imaging studies examining the effects of marijuana on the human brain have

ranged in technique and focus. Some studies have focused on the acute effects of the

main psychoactive ingredient, Δ9-THC. These studies often examine the brain after

administration of various doses of marijuana or Δ9-THC to volunteers. Other studies

have examined the long-term effects of marijuana, relying on data from heavy marijuana

14

users either seeking treatment or recruited from the general population. Some studies

address the effects of marijuana on resting-state brain activity, while others examine how

marijuana disrupts activity during simple or more complex cognitive tasks. These studies

reveal how marijuana and its cannabinoid compounds alter the brain following acute and

heavy use. It is worth noting that comparing data across these studies, especially data

from studies performed in MJ Users, should be approached with caution. These studies

not only rely on different imaging methods, but they often employ inconsistent analysis

techniques and study designs. For example, many studies differ in their operational

definition of heavy marijuana use and, more importantly, in the amount of time that has

lapsed between acquiring imaging data and a participant’s last marijuana use. That being

said, these studies often report compromised structure and function in consistent limbic

and cortical brain regions involved in both cognitive and emotional processing, and these

data often parallel reports from animal studies.

Marijuana and Brain Structure and Function

Acute Marijuana and Resting State Brain Activity. Several studies have

identified acute changes in resting state brain activity to both smoked marijuana and

intravenously (i.v.) administered Δ9-THC. In a series of early SPECT studies increased

cerebral blood flow (CBF) was observed in the right frontal cortex and temporal cortex

following smoked marijuana (Δ9-THC range: 1.75 – 3.55%) in recreational users, relative

to placebo (Mathew and Wilson 1993; Mathew et al. 1992). In MJ Users, greater CBF

was found in bilateral frontal and left temporal brain areas in the presence of an overall

global decrease in CBF, relative to recreational users (Mathew et al. 1989). Using PET

imaging, Volkow et al. (1996) reported lower baseline metabolism in the cerebellum of

15

MJ Users, compared to Controls. Following i.v. administration of Δ9-THC (2mg),

however, cerebellar metabolism increased in both groups, with increases in orbitofrontal

cortex, prefrontal cortex and basal ganglia were specific to MJ Users (Volkow et al.

1996). In another PET study, recreational users given i.v. Δ9-THC (0.15 / 0.25mg)

showed increased resting state metabolism in bilateral frontal, insular and anterior

cingulate cortices (ACC), relative to placebo (Mathew et al. 1997). The increases in the

frontal cortex, insula and ACC were replicated in several other PET studies in which i.v.

Δ9-THC (0.15 / 0.25mg) was administered to recreational users (Mathew et al. 1999;

Mathew et al. 1989) and MJ Users (Mathew et al. 2002) and compared to baseline.

Reductions were observed in CBF of the cerebellum, basal ganglia, thalamus,

hippocampus and amygdala of recreational users (Mathew et al. 1999; Mathew et al.

1989). In a recent PET study examining the relationship between Δ9-THC and the

dopamine neurotransmitter system during the resting state, decreases in [11

C]Raclopride

binding where observed in the ventral striatum and precommissural dorsal putamen after

recreational users inhaled Δ9-THC (8mg). Together these data demonstrate that

marijuana and Δ9-THC have the ability to acutely effect resting state blood flow in brain

areas involved in executive function and emotional processing in both recreational users

and MJ Users. These data also suggest that the reinforcing effects of Δ9-THC in

recreational users may result from increased activity in dopaminergic reward pathways.

Acute Marijuana and Cognitive Tasks. To examine how cannabinoids interfere

with normal function when the brain is challenged to perform a task, both PET and fMRI

studies have examined the acute effects of marijuana and its major cannabinoid

compounds. In recreational users performing a dichotic listening task, PET imaging

16

revealed that smoked marijuana (Δ9-THC = 20mg) produced significant signal increases

in the bilateral temporal lobe, left ventral frontal cortex, orbital frontal cortex, ACC, right

insula and putamen and bilateral cerebellum, relative to baseline or placebo (O'Leary et

al. 2002; O'Leary et al. 2007). Decreases were observed in bilateral frontal areas, the left

STG, and right occipital lobe (O'Leary et al. 2002). In a self-paced counting task, both

moderate users and MJ Users had increased activity in the left orbital frontal cortex, ACC

and right cerebellum and decreases in portions of the frontal lobe, temporal lobe and right

occipital lobe, compared to baseline (O'Leary et al. 2003). In an fMRI study using drug

naïve participants performing a motor inhibition task, Borgwardt and colleagues (2008)

observed that oral Δ9-THC (10mg) increased activity in the right hippocampus and

parahippocampal gyrus as well as the right superior transverse temporal gyrus and left

posterior cingulate cortex (PCC) and deceased activity in the right inferior frontal cortex

and right ACC, compared to placebo (Borgwardt et al. 2008). Two recent fMRI studies

have examined the influence of Δ9-THC during emotional processing. In a whole brain

fMRI analysis, oral Δ9-THC (10mg) given to naïve users viewing emotional faces

resulted in increased activity in bilateral portions of the frontal cortex and right parietal

cortex and decreased activity in the ACC, (PCC), left amygdala and right cerebellum,

compared to placebo (Fusar-Poli et al. 2009). Similarly, examining emotional reactivity

specifically within the amygdala, Phan and colleagues (2008) found that recreational

users treated with oral Δ9-THC (7.5mg) had altered processing of social signals of threat

(angry faces > happy faces). Specifically, compared to placebo, Δ9-THC reduced the

natural fear response to threatening stimuli in the right amygdala (Phan et al. 2008).

17

These data are similar to diminished fear-responses observed in animals receiving

cannabinoid agonists (Lin et al. 2006; Marsicano et al. 2002).

Chronic Marijuana and Brain Structure. Studies examining structural brain

changes in MJ Users have yielded conflicting results. In one voxel-based study, it was

reported that current, frequent, young adult users had no evidence of atrophy or changes

in brain tissue volumes or composition, compared to Controls (Block et al. 2000a).

Another study agreed with these negative findings (Tzilos et al. 2005), and in specific

region of interest (ROI) analyses, two studies reported no structural abnormalities in the

hippocampus (Tzilos et al. 2005) or parahippocampal gyrus (Jager et al. 2007), structures

with high densities of CB1 receptors and associated with normal memory function. In a

diffusion tensor imaging (DTI) study of adolescent heavy users, it was reported that there

was no evidence of cerebral atrophy or loss of white matter integrity in users, compared

to Controls (Delisi et al. 2006). In another DTI study with slightly older participants,

heavy users were found to be no different from Controls in fractional anisotropy (FA), an

indirect measure of white matter integrity, but showed a non-significant trend towards a

difference in mean diffusivity (MD), a measure of overall isotropic water diffusivity in

frontal lobe white matter (Gruber and Yurgelun-Todd 2005). On the other hand, a voxel-

based morphometry study reported that MJ Users had altered tissue composition in

several areas, compared to Controls (Matochik et al. 2005). Specifically, it was found

that heavy users had greater gray matter densities bilaterally in the precentral gyrus and

thalamus and lower densities in the right parahippocampal gyrus. Higher white matter

density was observed adjacent to the left parahippocampal gyrus and fusiform gyrus and

lower densities were observed in portions of the left parietal lobe. In the same study, the

18

duration of marijuana use was positively correlated with white matter density in the left

precentral gyrus, suggesting that the length of marijuana use predicted some of the

observed structural changes (Matochik et al. 2005). In a recent high resolution voxel-

based ROI study, both the bilateral amygdala and hippocampus was found to have

reduced volumes in MJ Users, compared to Controls (Yucel et al. 2008), suggesting that

heavy marijuana use across protracted periods have harmful effects on these emotion and

memory structures. Finally, a recent DTI study found significantly increased MD in the

corpus callosum adjacent to prefrontal gray matter structures (Arnone et al. 2008),

suggesting that MJ Users may also have damaged white matter tracts that could interfere

with prefrontal executive functions such as decision-making abilities.

Chronic Marijuana and Resting State Brain Activity. An early SPECT

imaging study reported decrease in global CBF in MJ Users during the early phase of

abstinence (average = 5 days) with increases observed over subsequent months of

abstinence (Tunving et al. 1986). Another SPECT study also reported decreased global

CBF following abstinence (average = 2 days) in heavy users (Lundqvist et al. 2001). In a

PET study examining resting state activity in heavy users following 26 hours of

monitored abstinence, increased activity was observed in the right ACC while decreases

were observed in bilateral cerebellum and prefrontal cortex (Block et al. 2000b). Another

study used Dynamic susceptibility contrast MRI (DSCMRI) in heavy users to examine

cerebral blood volume (CBV) at three time points during monitored abstinence (Day 0 =

6 – 36 hrs following use; Day 7 and Day 28). This study reported that at seven days of

abstinence, compared to Controls, heavy users had increased (CBV) in the right frontal

cortex, bilateral temporal cortex and cerebellum. At 28 days, only the left temporal area

19

and the cerebellum showed increases, suggesting that CBV levels may normalize after

extended abstinence (Sneider et al. 2008). In a PET study examining reward systems in

marijuana dependent 18 – 21 year old males that had been abstaining for at least 12

weeks, while no differences were observed in D2/D3 receptor availability, compared to

Controls, the marijuana dependent abstaining group had lower normalized glucose

metabolism in the right orbital frontal cortex, bilateral putamen and precuneus (Sevy et

al. 2008). While the studies on resting state activity offer insight into the dynamic

changes in normal blood flow during extended periods of abstinence and give hope that

resting function may normalize following cessation of drug use, there is little known

about resting state function in non-treatment seeking heavy users who continue to use

marijuana on a daily basis.

Chronic Marijuana and Cognitive Tasks. One study investigating activity

during a simple finger-sequencing task, four to six hours following their last marijuana

use, found that heavy users had decreased activity in bilateral SMA and ACC, compared

to Controls (Pillay et al. 2004). Another study comparing activity during a visual

attention task between Controls and two different groups of heavy users, one abstaining

and one actively using (last use: 4 – 24 hrs before scanning), found that despite similar

behavioral performance in all groups, the marijuana groups had decreased activity in

right prefrontal, dorsal parietal and medial cerebellar regions, compared to Controls

(Chang et al. 2006). Comparing activity between the two marijuana groups revealed that

the actively using individuals had increased activity in frontal and medial cerebellar

regions, compared to the abstaining group (Chang et al. 2006). These data highlight that

it is very possible that in the absence of observable behavioral differences between

20

Controls and MJ Users there can be underlying differences in functional brain activity.

Similarly, two imaging studies, while reporting somewhat conflicting results, found that

as both MJ Users and Controls performed a stroop task to adequate levels, MJ Users had

altered brain activity, compared to Controls. A PET study showed that MJ Users

(abstinent for 25 days) had increased signal in bilateral hippocampus, right precentral

gyrus and left occipital lobe and decreased signal in bilateral DLPFC, right VMPFC and

ACC during task performance (Eldreth et al. 2004). While the fMRI study reported that

heavy users (abstinent for at least 12 hrs) had increased activity in the right DLPFC and

bilateral ACC while performing the task (Gruber et al. 2009). Though these data are

somewhat conflicting, they both lend themselves to the interpretation that individuals

who use heavy amounts of marijuana may be in a functionally neuroadaptive state.

Marijuana use has been associated with deficits in memory and several studies

have focused on identifying the neural correlates of various types of faulty memory MJ

Users. One PET study found that MJ Users relied more on short-term memory, recalling

23% more words from the latter portion of memorized word list and 19% less from the

former section, compared to Controls (Block et al. 2002). While performing this verbal

memory task, MJ Users had decreased signal in bilateral PFC and increased signal in the

cerebellum as well as altered lateralization in the hippocampus, compared to Controls

(Block et al. 2002). In an fMRI study examining working memory, MJ Users (abstinent

for > 7 days before testing) showed no deficit in task performance but had increased

activity in the left superior parietal cortex while performing the working memory task,

compared to Controls (Jager et al. 2006). Another study by the same group examining

hippocampal-dependent associative memory and found no difference in behavior but

21

decreased activity in parahippocampal regions and the right DLPFC of heavy users

(abstinent for > 7 days before testing), compared to Controls (Jager et al. 2007). This

again highlights the possibility that underlying functional abnormalities may exist in MJ

Users the absence of observable behavioral differences. In a study examining activity

during a spatial working memory task, MJ Users (6 – 36 hrs after last use) had increased

activity in portions of the inferior and superior frontal lobe, bilateral middle frontal gyrus

and right STG and while portions of the bilateral middle frontal gyrus also showed

decreased activity, compared to Controls (Kanayama et al. 2004). The authors

interpreted the widespread increases in activity of MJ Users as brain areas having to

“work harder” in order to perform the task.

Their have been two imaging studies in MJ Users directly related to the topics

addressed in this dissertation, complex decision-making and affective processing. One

study used PET to examine brain activity in MJ Users (abstinent for 25 days) performing

IGT (Bolla et al. 2005). The other study used fMRI to examine responsivity of MJ Users

(abstinent for > 12 hrs) to emotional faces presented below the level of consciousness

(Gruber and Yurgelun-Todd 2005). In the former study, eleven 25-day abstinent MJ

Users exhibited increased activation in the cerebellum and decreased activation in the

right lateral OFC and DLPFC while performing the IGT, compared to Controls (Bolla et

al. 2005). The authors interpreted the relative decreases in frontal activity in MJ Users as

compromised executive function in MJ Users and hypothesized that MJ Users “may

focus on only the immediate reinforcing aspects of a situation while ignoring the negative

consequences” (Bolla et al. 2005). Due to the PET imaging technique used in the study,

however, the authors did not have the ability to isolate the specific brain responses to the

22

positive (monetary gains) and negative (monetary losses) events to directly test this

hypothesis. The experiments in this dissertation will address this hypothesis by utilizing

event-related fMRI to examine neurofunctional responses during specific components of

the decision-making process. In the other study directly relevant to the experiments in

this dissertation, Gruber et al. (2009) observed that when emotional faces were presented

to MJ Users below the level of consciousness, they had decreased functional responses in

portions of the amygdala and dorsal ACC, compared to Controls (Gruber et al. 2009).

This study demonstrates that MJ Users have blunted functional responses to affective

stimuli presented below the level of awareness, but does not address whether MJ Users

have decreased responses for stimuli consciously judged to be emotional. In effort to

extend findings and better understand the nature of affective processing in MJ Users, the

studies contained in this dissertation use event-related fMRI to examine brain

neurofunctional responses to stimuli judged to be emotional.

Statement of Purpose

The present studies were designed to: 1) identify the components of the decision-

making process that account for poor performance in MJ Users and to 2) examine the role

of affective information processing in strategy development. They also seek to 3)

understand conscious emotional processing in MJ Users. To address these aims, event-

related fMRI analyses were performed using a modified version of the IGT task.

Behavior and brain activity were recorded throughout the task and activity associated

with various portions of the decision-making process was isolated. Initial analyses

focused on brain activity during the selection and evaluation components of decision-

making. Subsequent analyses focused on the specific role of positive (monetary gains)

23

and negative (monetary losses) feedback during the strategy development phase of

problem solving. In these experiments it was hypothesized that poor performance on the

IGT would be related to functional insensitivities to the monetary losses that aid strategy

development.

In a second experiment, event-related fMRI was conducted as MJ Users

performed a simple emotional task. Participants viewed stimuli obtained from the IAPS

database and judged their emotional value. It was hypothesized that, relative to Controls,

MJ Users would judge fewer stimuli to contain emotional value. Furthermore, for stimuli

considered to be emotional, MJ Users would have a blunted functional response in brain

areas known to be involved in processing emotional information and making judgments.

24

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41

CHAPTER II

ALTERED FUNCTIONAL ACTIVITY IN CHRONIC MARIJUANA USERS

DURING SPECIFIC COMPONENTS OF DECISION-MAKING

Michael J. Wesley1, Colleen A. Hanlon

1, Matthew McConnell

2, Linda J. Porrino

1,3

1 Department of Physiology and Pharmacology

2 School of Medicine

3 Center for the Neurobiological Investigation of Drug Abuse

Wake Forest University

Winston-Salem, NC 27157 (U.S.A.)

42

ABSTRACT

Chronic marijuana users (MJ Users) have been found to have deficits in complex

decision-making abilities. Given that decision-making is a multifaceted process

involving brain activity in independent and integrative neural systems, the source of this

deficit is unclear. The purpose of this functional neuroimaging study was to determine

the specific components of a complex decision making task in which MJ Users have

functional deficits. Sixteen MJ Users and 16 Controls performed a modified version of

the Iowa Gambling Task (IGT) which required participants to select from several decks

of cards, and then evaluate monetary wins and losses from decks before making the next

selection. Consistent with prior studies, Controls adopted successful decision-making

strategies, while MJ Users made persistently disadvantageous choices. Results

demonstrate that during selections, MJ Users had elevated activity in multiple brain

regions, compared to Controls. During evaluation, however, MJ Users had less activity

than Controls. These data suggest that poor decision-making in MJ Users is associated

with functional insensitivities while evaluating the consequences of their choices.

43

INTRODUCTION

Marijuana continues to be the most commonly used illegal drug in the United States

today. With the increase in both medicinal and recreational use, the perception that

marijuana is harmful has declined (Johnston et al. 2009). As a result, there is growing

interest in determining the potential consequences of long-term use. Deficits in processes

such as cognition, learning and memory, executive function, attention, time perception,

and emotion (Gruber et al. 2009; Solowij et al. 1991; 1995; Whitlow et al. 2004) have

been documented in heavy marijuana users (MJ Users). Many of these deficits have been

shown to be accompanied by significant changes in both brain structure and function.

One such deficit in executive function is revealed by performance on the Iowa Gambling

Task (IGT), a complex decision-making task where “real-world” contingencies, winning

and losing money, guide behavior towards maximizing money over time (Bechara et al.

1994). The IGT is composed of selection and evaluation components during which

participants select cards randomly from four card decks under ambiguous conditions and

then evaluate feedback detailing the monetary consequences of their choices. Each task

component requires multiple on-going processes. During selection participants view

choice options, and initiate and execute timed choices that reflect individual strategies to

solve the task. Each selection produces a monetary gain or win, but occasionally

selections also produce a monetary loss. During evaluation, participants view the

positive and negative consequences of their choices and integrate this affective

information into on-going cognitive processes. Consequences must be attended to,

compared with memory of task instructions and previous choice outcomes, and

incorporated into updating strategies to solve the task. According to the win and loss

44

contingencies associated with each deck, two decks emerge as advantageous and two

disadvantageous. Disadvantageous decks produce larger immediate gains but larger

losses over time while advantageous decks yield smaller immediate gains but smaller

losses over time. Successful performance requires that during the task participants begin

to shift the majority of their selections from disadvantageous to advantageous decks,

accepting smaller immediate gains with less long-term monetary loss. Performance is

typically measured by a Net Score which reflects the difference between advantageous

and disadvantageous selections. MJ Users when compared to healthy controls (Controls)

have been shown to perform particularly poorly on this task, consistently making more

disadvantageous than advantageous choices (Hermann et al. 2009; Whitlow et al. 2004).

In recent years several neuroimaging studies have used the IGT to examine brain activity

during complex decision-making in various populations, including healthy Controls

(Christakou et al. 2009; Fukui et al. 2005; Lawrence et al. 2009; Li et al. 2010; Lin et al.

2008; Northoff et al. 2006), patients with brain injuries (Fujiwara et al. 2008; Gupta et al.

2009) and diseases (Ibarretxe-Bilbao et al. 2009) as well as individuals with psychiatric

(Nakamura et al. 2008; Premkumar et al. 2008) and drug abuse disorders (Gruber and

Yurgelun-Todd 2005; Tanabe et al. 2007; Tucker et al. 2004). A recent study in healthy

adults demonstrated that performance on the IGT is associated with activity in brain areas

involved in executive function and the integration of affective and cognitive processing,

including the striatum, anterior and posterior cingulate cortex, the dorsal lateral prefrontal

cortex (DLPFC), insula, and the orbital and ventral medial prefrontal cortex (Li et al.

2010).

45

The IGT is, however, a complex task that involves both a selection phase where

participants make choices followed by a feedback phase where they receive information

about wins and losses. These two phases are likely to involve distinct cognitive

processes that are subserved by discrete neural circuits; and poor performance might be

attributed to deficits in either or both components of the task. To date, only a few studies,

however, have used event-related fMRI to disentangle functional activity relevant to

specific components of the task. One such study in healthy adults found elevated activity

in the striatum and insula during selection, and elevated activity in the inferior parietal

cortex during evaluation (Lin et al. 2008). Evaluation of the largest monetary losses was

associated with elevated medial prefrontal cortex activity (Lin et al. 2008). Another

study in healthy adults reported elevated anterior cingulate and medial prefrontal cortex

activity during selections involving risk, compared to safe selections (Fukui et al. 2005).

One goal of this study, therefore, is to use event-related functional magnetic resonance

imaging (fMRI) to identify differences in brain activity between Controls and MJ Users

during specific components (selection and evaluation) of the IGT. Furthermore, a second

goal was the evaluation of functional activity associated with specific event types within

each component (advantageous or disadvantageous selections and win or loss evaluation)

of the task. We found that MJ Users had greater functional responses in cognitive- and

emotion-related brain areas while implementing choices, but smaller functional responses

in these areas while receiving feedback about on-going performance.

46

METHODS

Participants

Sixteen right-handed chronic marijuana users (MJ Users) and 16 non-marijuana-smoking

controls (Controls) were included in this study (see Table 1). Participants responded to

local media advertisements and participated in an initial phone screen. Those who met

initial inclusion criteria over the phone were invited into the laboratory and agreed to

participate in procedures approved by the Wake Forest University School of Medicine

Institutional Review Board. During an initial visit, participants provided urine samples to

test for pregnancy and drug use and were administered the Wechsler Abbreviated Scale

of Intelligence (WASI; (Wechsler 1999), and the Structured Clinical Interview for DSM-

IV (SCID; (First 1997). Participants were excluded if they had a history of head trauma,

neurological disorders, Axis-I psychiatric disorders (other than marijuana dependence for

the MJ Users), current abuse of substances other than nicotine, or an I.Q. of less than 80.

MJ Users tested negative for illicit drugs other than marijuana and Controls tested

negative for all illicit drugs. Participants who passed all inclusion criteria were scheduled

for a second visit to be scanned while performing the IGT. MJ Users were asked to

abstain from using marijuana the night before the scheduled scan visit starting at

midnight.

Procedure

On the day of the functional MRI scan, participants provided urine samples to test for

pregnancy and drug use and completed anxiety (Speilberger Test of Anxiety) and

depression (Beck’s Depression Inventory) inventories. Members of the MJ Users group

47

verbally acknowledged to abstaining from marijuana since the prior evening, and

provided the time, in hours, since their last marijuana use. During this time MJ Users

also did not report or overtly exhibit signs of marijuana withdrawal (Budney and Hughes

2006).

One hour before being scanned, participants were trained on the IGT using a standard

laptop computer and response box. Participants visually followed as task instructions

presented on the computer screen were read aloud by a study technician. Immediately

following the instructions, participants completed a trial run of the task containing 8

gambling events and 2 control events in order to become familiar with the timing and

layout of the task. During the trial run, monetary win and loss contingencies associated

with deck selections were randomized in order to avoid strategy carryover to the scanner

task. Participants were made aware of this and performed an additional trial run if there

were multiple mistimed events in the first trial run. Although previous studies have

found no difference between smokers and nonsmokers on IGT performance (Harmsen et

al. 2006; Lejuez et al. 2003), approximately one hour before the acquisition of their

functional scans participants were given a 15 minute break with the opportunity to smoke

a cigarette to avoid potential confounds of nicotine withdrawal on functional brain

activity (Wang et al. 2007; Xu et al. 2007). Three participants in the MJ Users group

took advantage of the opportunity to smoke a cigarette.

Iowa Gambling Task (IGT)

In the fMRI scanner, a modified version of the IGT was presented on MR compatible

goggles, and responses were recorded on a button box positioned under the right hand.

48

Before task onset participants followed along once more as instructions were read aloud.

When participants verbally acknowledged comprehension of the instructions, the task

began following a 20 second countdown. Each participant performed three sections (i.e.

RUNS) of the task, each consisting of 45 gambling events and 13 randomly inserted

control events. RUN 1 consisted of events 1 through 58. RUN 2 consisted of events 59

through 116, and RUN 3 encompassed events 117 through 174.

Examples of IGT event types can be seen in Fig. 1. When the task started participants

were instructed to “Select a Deck” for a fixed 2 second period via a text box on the upper

Figure 1. Iowa gambling task (IGT) event types. During the

selection component, participants were instructed to “Select a

Deck” for 2 seconds from one of four decks of cards

(A,B,C,D). Following each selection, participants evaluated

feedback regarding the amount of money won and / or lost

during that gambling event.

49

left-hand side of the screen. During this selection component participants chose a card

from one of four card decks labeled A, B, C and D by pressing buttons 1, 2, 3 or 4 on the

button box, respectively. Immediately following the selection component, participants

evaluated feedback for a variable period of time jittered around 2 seconds. The length of

the evaluation component was jittered to correct for stimulus onset asynchrony and to

assure adequate sampling of the HDR for imaging analyses. During the evaluation

component, feedback detailing the monetary gain and/or loss associated with the

selection replaced selection instructions in the text box on the upper left-hand side of the

screen. The end of each evaluation component signaled the onset of the next selection

component. On the upper right-hand side of the screen an ongoing monetary score was

updated following each deck selection. During selection control events participants were

instructed to select a card from a specific deck (e.g. “Select Deck C”). Following

selection control events, participants received evaluation control events that read “You

Neither Win Nor Lose”. For all imaging analyses the functional brain activity associated

with the selection component (i.e. choice) and the evaluation component (i.e.

consequence of choice), was independently isolated by comparing activity during each

component with activity during their respective control events.

Selections from decks A and B resulted in immediate gains of $100 each with losses over

time ranging from $250 to $1200 and were considered disadvantageous. Selections from

decks C and D produced immediate gains of $50 with losses over time ranging from $50

to $250 and were considered advantageous. The proportion of responses allocated to

disadvantageous (A and B) and advantageous (C and D) decks were recorded for each of

the three RUNS and used to examine behavioral performance.

50

Functional MRI data acquisition

Images were acquired on a 1.5T General Electric scanner with a birdcage-type standard

quadrature head coil and an advanced nuclear magnetic resonance echoplanar system.

Foam padding was used to limit head motion. High-resolution T1-weighted anatomical

images (3D SPGR, TR=10 ms, TE=3 ms, voxel dimensions 1.0×1.0×1.5 mm, 256×256

voxels, 124 slices) were acquired for co-registration and normalization of functional

images. During each of the three RUNS of the task, 162 co-planar functional images

were acquired using a gradient echoplanar sequence (TR=2100 ms, TE=40 ms, voxel

dimensions 3.75×3.75×5.0 mm, 64×64 voxels, 28 slices) for a total of 486 functional

volumes of data. The scanning planes were oriented parallel to the anterior–posterior

commissure line and extended from the superior extent of motor cortex to the base of the

cerebellum. Six volumes of data were acquired during the 20 second countdown period

and immediately discarded to allow for equilibrium before selections began.

Statistical Analyses

Demographics and Behavior

Independent samples t-tests were used to compare groups on parametric demographic

variables. Chi-square analyses were used to compare groups on the proportion of

participants who were male and female and who were cigarette smokers. To examine

overall task performance a Total Net Score was calculated for each individual by

subtracting the total number of selections made on disadvantageous decks (A and B)

from the total number made on advantageous decks (C and D). A positive score,

therefore, reflected more advantageous deck selections, relative to disadvantageous

51

selections on the task. An independent samples t-test was used to determine if overall

task performance differed between Controls and MJ Users. Behavioral data were

analyzed using the Statistical Package for the Social Sciences (SPSS) version 11.5.

Functional MRI preprocessing and data analysis

The functional data from each participant were corrected for acquisition time (slice

timing), realigned to the first volume (motion correction), normalized into a standardized

neuroanatomical space (Montreal Neurological Institute brain template), smoothed using

a Gaussian kernel of 8 mm, and high-pass filtered (128s) to remove low frequency noise.

Inspection of motion correction revealed that all corrections were less than the 2mm. For

each individual, a multiple linear regression analysis was performed using all 486

functional volumes. Functional data for six different conditions throughout the IGT were

isolated. Conditions included: 1) advantageous and 2) disadvantageous selection events,

3) monetary win and 4) loss evaluation events, as well as 5) selection control events and

6) evaluation control events. All conditions were modeled by convolving the onsets of

relevant event times with a canonical hemodynamic response function. Trials in which

no response was made were excluded from the analyses.

For each individual, statistical contrast maps of activity during the selection component

of the task were made by comparing activity during all selections to that of selection

control events (advantageous + disadvantageous selection events > selection control

events). Similarly, contrast maps of activity during the evaluation component of the task

were made by comparing activity during all evaluations to that of evaluation control

events (win + loss evaluation events > evaluation control events). For each individual,

52

contrast maps of advantageous selections (advantageous selection events > selection

control events) and disadvantageous selections (disadvantageous selection events >

selection control events) were made as well as maps corresponding to win evaluation

(win evaluation events > evaluation control events) and loss evaluation (loss evaluation

events > evaluation control events).

Within and between-group analyses were performed to identify brain activity associated

with overall IGT components (selection and evaluation) and their specific event types

(advantageous and disadvantageous selections; win and loss evaluation). Within-group

analyses were performed on contrast maps of all selection and all evaluation using voxel-

wise P thresholds of 0.001 further adjusted at the cluster level (P < 0.05, corrected for

multiple comparisons; minimum extent threshold of 100 voxels). Between-group

analyses were performed on selection and evaluation component contrast maps,

advantageous and disadvantageous selection contrast maps, and win and loss evaluation

contrast maps. Between-group analyses were performed using voxel-wise thresholds of

0.01 further adjusted at the cluster level (P < 0.05, corrected for multiple comparisons;

minimum extent threshold of 100 voxels). All imaging analyses were performed with

SPM 5 (Wellcome Department of Imaging Neuroscience, London, UK) in the MATLAB

7.0 (Mathworks, Natick, MA) shell using an event-related model (Friston et al. 1998).

53

RESULTS

Demographics

A description of study participants is shown in Table 1. There was no difference between

groups for several demographic variables including age, I.Q., sex, alcohol intake, caffeine

usage, and anxiety or depression scores. Controls were 26.6 ± 6.1 years old and did not

meet dependence criteria for any illicit drugs. Four of 16 members of the Control group

reported previous marijuana use with use limited to fewer than 50 lifetime uses, occurring

more than 2 years prior to the study. MJ Users were 25.9 ± 3.4 years old (mean ± sd) and

reported using marijuana 2.3 ± 1.4 times a day, 29.4 ± 1.0 days a month, for 9.1 ± 3.8

years. The average age of first marijuana use was 16.0 ± 2.5 years. Four of the sixteen

members of the MJ Users group met criteria for marijuana dependence. On the scanning

day, all participants had negative urine screens for illegal substances (other than

marijuana in MJ Users). All members of the MJ Users group tested positive for

marijuana metabolites the day of scanning and reported a mean (± sd) abstinence from

marijuana of 12.0 ± 2.3 hours (range = 8.5 – 16 hours). A significantly greater proportion

MJ Users were cigarette smokers (8 of 16) than Controls (2 of 16) (X2 = 5.24, p = .022).

54

Table 1. Group Demographics

Variable

Controls

(N = 16)

MJ Users

(N = 16)

Mean (±

SD)

Mean (±

SD)

t / X2

Valuep

Age (years) 26.6 (6.1) 25.9 (3.4) 0.39 n.s.

I.Q. 115 (8.4) 107.8 (13.6) 1.82 n.s.

Sex 0.29 n.s.

Male 6 9

Female 10 7

Cigarette Smokers 12.5 % 50 % 5.24 0.022

Alcohol AUDIT Score 2.8 (2.0) 4.2 (2.3) 1.91 n.s.

Caffeine (mg/day) 96.9 (99.1) 107.8 (96.5) 0.32 n.s.

Spielberger State

Anxiety24.6 (5.6) 27.0 (6.5) 1.10 n.s.

Beck’s Depression 2.5 (4.0) 3.9 (3.5) 1.04 n.s.

Marijuana Use:

Age of onset (years) 16.0 (2.5)

Years of Total Use 9.1 (3.8)

Days per month 29.4 (1.0)

Times per day 2.3 (1.4)

Years at current use

level4.7 (3.7)

55

IGT Behavioral Performance

Behavioral performance on the IGT is shown in Fig. 2. A significant group difference

was observed in overall task performance with Controls performing significantly better

than MJ Users t(30) = 2.16, p = .039. The mean (± se) Total Net Score in Controls was

4.43 ± 10.3, compared to −20.56 ± 5.3 in MJ Users.

Figure 2. Behavioral performance on the Iowa Gambling

Task. Controls (unfilled) made significantly more

advantageous deck selections compared to chronic marijuana

users (MJ Users; filled) † = p < 0.05

56

Functional Imaging During Components of the IGT

Selection

Analysis of brain activity during the selection component of the IGT, during which time

participants viewed and selected among the 4 decks of cards, is shown in Table 2 and Fig.

3. In Control participants, a comparison of selection events to control or no choice events

revealed significant bilateral activation in the ventral striatum extending caudally into

medial thalamic areas and in visual processing regions (Table 2; Fig. 3a). In contrast,

contrasts in MJ Users during the selection component of the IGT showed significant

activations in a wider range of brain regions (Table 2; Fig. 3a) including bilateral visual

processing regions, striatum also extending caudally to include thalamus, insula, anterior

cingulate, and right precuneus. In addition there was significant activation in the left

hemisphere in areas 3 and 4. A direct comparison of brain activation of Controls and MJ

Users during the selection component (Table 2, Fig. 3b) showed that MJ Users had

significantly greater activity in multiple brain regions relative to Controls. The most

prominent cluster was in the left insular cortex (Fig. 3b [Y = −7, 5]) and extended

caudally and ventrally to include portions of the parahippocampal gyrus of the medial

temporal lobe (Fig. 3b [Y = −7]). There were no areas during the selection component

where Controls had significantly greater activity than MJ Users.

Further evaluation of the selection component was carried out by separating selection

events into advantageous and disadvantageous choices (Table 3; Fig. 3c). Controls and

MJ Users did not differ in activity while selecting from advantageous decks. During the

selections from disadvantageous decks, however, MJ Users MJ Users had greater activity

57

in portions of the postcentral gyrus that extended medially to include the posterior

cingulate cortex and dorsally and caudally to include somatosensory association cortices

MJ Users also had greater activity in the right insula and parahippocampal gyrus during

disadvantageous selection periods, compared to Controls. There were no areas where

Controls had greater activity than MJ Users during disadvantageous selection events.

Overall differences between Controls and MJ Users during the selection component of

the IGT, therefore, were predominantly the result of differences during the selection from

the disadvantageous decks.

58

Figure. 3 Brain activity during the selection component of the IGT.

a) Functional clusters of activity in Controls and chronic marijuana

users (MJ Users) during all selection events, compared to selection

control events (selection > selection control events). b) Clusters

where activity during selection was greater in MJ Users, compared

to Controls. There were no clusters where activity during selection

was less in MJ Users, compared to Controls. c) Clusters where

activity during disadvantageous selections was greater in MJ Users,

compared to Controls. There were no clusters where activity during

disadvantageous selections was less in MJ Users, compared to

Controls. There were no group differences in activity during

advantageous selections.

59

Table 3. Clusters of significant BOLD activity during advantageous (advantageous > control events) and disadvantageous

(disadvantageous > control events) selection on the IGT between Controls and MJ Users (fig. 3c).

Analysis Side Anatomical Regions BA*MNI Coordinates+

(x, y, z)

Maximum

Voxel

t-value

Advantageous Selection

MJ Users < Controls N/A no suprathreshold cluster

MJ Users > Controls N/A no suprathreshold cluster

Disadvantageous Selection

MJ Users < Controls N/A no suprathreshold cluster

MJ Users > Controls R Postcentral Gyrus

(Somatosensory Cortex)

(Posterior Cingulate Cortex)

3

(5)

(23)

24 -40 64 4.27

R Insula

(Parahippocampal Gyrus)

13

(48)

40 -24 22 4.07

*BA, Brodmann areas. Listed areas correspond to location of the maximum voxel of activation and other BAs associated with the

activity cluster are in parentheses. +MNI, Montreal Neurological Institute. Regions and coordinates correspond to the maximum

voxel of activity within the cluster. Additional Brodmann areas associated with the activity cluster are listed in parentheses.

Table 2. Clusters of significant BOLD activity during IGT selection (selection > control events) in Controls and MJ Users

independently (fig. 3a), and compared directly between the groups (fig. 3b).

Analysis Side Anatomical Regions BA*MNI Coordinates+

(x, y, z)

Maximum

Voxel

t-value

Selection

Controls R Caudate 6 6 0 10.65

R Occipital Lobe 17 (18) 14 -84 12 7.17

L Occipital Lobe 18 (19) -22 -96 -6 5.64

MJ Users L Postcentral Gyrus 3 (4) -42 -26 68 7.37

R Medial Globus Pallidus (Caudate) 10 -2 4 7.17

L Occipital Lobe 19 -46 -72 -12 6.61

R Occipital Lobe 17 (18, 19) 23 -98 -5 6.44

L Insula 13 -40 14 -6 6.09

R Cingulate Gyrus 32 (8, 9) 4 20 44 5.97

R Precuneus 7 6 -74 44 5.30

R Insula 13 34 20 2 5.14

MJ Users < Controls N/A no suprathreshold cluster

MJ Users > Controls L Insula (Parahippocampal Gyrus) 13 (48) -50 6 8 3.92

*BA, Brodmann areas. Listed areas correspond to location of the maximum voxel of activation and other BAs associated with the

activity cluster are in parentheses. +MNI, Montreal Neurological Institute. Regions and coordinates correspond to the maximum

voxel of activity within the cluster. Additional Brodmann areas associated with the activity cluster are listed in parentheses.

60

Evaluation

Analysis of brain activity during the evaluation phase of the IGT, during which

participants receive feedback about the wins and losses that resulted from their selections,

is shown in Table 4 and Fig. 4). Overall both groups displayed greater activation during

evaluation than during the selection phase of the task (compare Fig. 3a and Fig. 4a). In

both control participants and MJ Users, a comparison of evaluation to Controls (no wins

or losses) events showed greater activity in bilateral precuneus the right fusiform gyrus

right inferior and middle temporal gyri and the portions of the left cerebellum Both

groups also exhibited large clusters of activity in bilateral portions of the middle frontal

gyrus that extended dorsally into the superior frontal gyrus (Fig. 4 [Y = 16, 31, 43]) and

rostrally to include the right dorsal lateral prefrontal cortex (DLPFC; Fig. 4 [Y = 43]).

However, unlike MJ Users, Controls displayed elevated activity in bilateral striatum, the

middle and anterior cingulate cortex and the left dorsolateral prefrontal cortex during the

evaluation phase.

A direct comparison of the brain activations of Controls and MJ Users during the

evaluation component (Table 4, Fig. 4b) revealed that MJ Users showed significantly less

activations than Controls in the posterior cingulate cortex that extended dorsally and

caudally to include portions of the precuneus region. In no brain areas did MJ Users

display greater activation than Controls during the evaluation component of the IGT.

Further evaluation of the evaluation component was carried out by separating evaluations

into win and loss events (Table 5; Fig. 4c). During the evaluation of wins, there were no

differences between the activations of Controls and MJ Users. During loss events,

however, MJ Users exhibited significantly less activations in the superior parietal cortex

61

and the posterior cingulate cortex as compared to Controls. This activity extended

dorsally and caudally to include somatosensory association cortices. There were no areas

in which MJ Users showed greater activity than Controls during the evaluation of losses.

62

Figure 4. Brain activity during the evaluation component of the IGT.

a) Functional clusters of activity in Controls and chronic marijuana

users (MJ Users) during all evaluation events, compared to

evaluation control events (evaluation > evaluation control events). b)

Clusters where activity during evaluation was less in MJ Users,

compared to Controls. There were no clusters where activity during

evaluation was significantly greater in MJ Users, compared to

Controls. c) Clusters where activity during monetary loss evaluation

was less in MJ Users, compared to Controls. There were no clusters

where activity during loss evaluation was greater in MJ Users,

compared to Controls. There were no group differences in activity

during win evaluation.

63

Table 4. Clusters of significant BOLD activity during IGT evaluation (evaluation > control events) within Controls and MJ Users

independently (fig. 4a), and compared directly between the groups (fig. 4b).

Analysis Side Anatomical Regions BA*MNI Coordinates+

(x, y, z)

Maximum

Voxel

t-value

Evaluation

Controls R Precuneus 7, 5 (18,19) 6 -72 54 12.25

R Middle Frontal Gyrus (DLPFC) 46 42 44 10 11.90

R Fusiform Gyrus 37 (7) 42 -60 -18 8.75

L Precuneus 7 (18,19) -32 -68 42 7.98

L Inferior Frontal Gyrus 45 -48 26 22 7.81

R Middle Temporal Gyrus (Inferior) 21 (20) 64 -24 -14 7.66

L Fusiform Gyrus 37 (7) -48 -62 -20 6.87

L Middle Frontal Gyrus (DLPFC) 6 (46) -26 14 58 6.57

L Cerebellum -36 -64 -44 6.51

L Putamen (Caudate) -18 8 4 6.15

R Putamen (Caudate) 16 10 4 5.39

R Anterior Cingulate Cortex 32 2 38 22 5.28

MJ Users R Precuneus 7 (18,19) 38 -44 44 11.84

R Middle Frontal Gyrus 6 32 6 58 9.24

L Cuneus (17,18) -16 -98 -6 8.96

R Middle Frontal Gyrus (DLPFC) 46 40 42 20 7.89

L Precuneus 7 (37) -30 -70 44 7.29

RInferior Occipital Gyrus (Fusiform

Gyrus)17 (18,19,37) 18 -90 -10 7.19

R Inferior Temporal Gyrus (Middle) 20 (21) 54 -56 -16 7.13

L Middle Frontal Gyrus 6 -38 8 56 6.64

L Cerebellum -46 -68 -36 6.55

MJ Users < Controls R Posterior Cingulate Cortex (Precuneus) 23 (5) -6 -32 26 4.08

MJ Users > Controls N/A no suprathreshold cluster

*BA, Brodmann areas. Listed areas correspond to location of the maximum voxel of activation and other BAs associated with the activity

cluster are in parentheses. +MNI, Montreal Neurological Institute. Regions and coordinates correspond to the maximum voxel of activity

within the cluster. Additional Brodmann areas associated with the activity cluster are listed in parentheses.

64

Table 5. Clusters of significant BOLD activity during win (win > control) and loss (loss > control) evaluation on the IGT between

Controls and MJ Users (fig. 4c).

Analysis Side Anatomical Regions BA*MNI Coordinates+

(x, y, z)

Maximum

Voxel

t-value

Win Evaluation

MJ Users < Controls N/A no suprathreshold cluster

MJ Users > Controls N/A no suprathreshold cluster

Loss Evaluation

MJ Users < Controls R Superior Parietal Lobule

(Somatosensory Cortex)

(Posterior Cingulate Cortex)

7

(5)

(23)

18 -68 60 6.83

MJ Users > Controls N/A no suprathreshold cluster

*BA, Brodmann areas. Listed areas correspond to location of the maximum voxel of activation and other BAs associated with the

activity cluster are in parentheses. +MNI, Montreal Neurological Institute. Regions and coordinates correspond to the maximum

voxel of activity within the cluster. Additional Brodmann areas associated with the activity cluster are listed in parentheses.

65

DISCUSSION

The findings of the present study demonstrate that MJ Users exhibit poor performance on

the Iowa Gambling Task that is accompanied by disruptions in functional brain activity

during multiple aspects of task performance. The behavioral deficits confirm previous

reports from this and other labs (Hermann et al. 2009; Whitlow et al. 2004) that have

shown that MJ Users fail to increase the number of selections from advantageous decks,

continuing to select disadvantageously, when compared to Controls. The differences in

the underlying neural circuitry recruited by Controls and MJ Users during IGT

performance were present in both the selection and evaluation aspects of the task.

Furthermore, in the selection phase, differences between the groups were largest for

disadvantageous selections and during the evaluation phase of the task, differences were

largest when feedback was provided about monetary losses. Considered together, these

data suggest that the inability of MJ Users to engage in advantageous decision making

may be closely related to the processing of negative (losses) and potentially negative

(disadvantageous choices) information.

Complex decision-making involves activity in independent and integrative neural

systems, and successful performance requires neural processes to work efficiently and in

accordance with specific task demands (Li et al. 2010). During the selection component

of the IGT, participants view options, and initiate and execute timed choices that reflect

individual strategies to solve the task. During the evaluation component, participants

view the positive and negative consequences of their choices (i.e. wins, losses and

monetary score) and integrate this affective information into ongoing cognitive processes.

Consequences must be attended to, compared with memory of task instructions and

66

previous choice outcomes, and incorporated into updating selection strategies.

Accordingly, the IGT engages neural activity in a distributed network of brain structures

involved in attention, memory, cognitive load, performance monitoring, and

emotional/somatic processing. The present findings demonstrate that MJ Users exhibit

dysfunctional activity in many of these systems resulting in the disrupted behavior.

During the selection component of the IGT, Controls and MJ Users had greater activity in

the striatum, thalamus and occipital lobe, relative to control events. This is consistent

with integrating visual spatial information and implementing behavioral decisions.

Activity in the ventral striatum may reflect the anticipation of reward that follows in the

evaluation component as activity in this area has been shown to increase while

anticipating various rewards (Knutson and Cooper 2005). Increased striatal activity may

also reflect increased anticipation to aversive stimuli, similar to increases observed in

Controls anticipating unpleasant cutaneous electrical stimulation (Jensen et al. 2003).

That Controls have a greater extent of striatal activity during selection may reflect greater

anticipation of consequences to come on the IGT, including monetary losses. Controls,

however, exhibited far less activation during this phase of the task than MJ Users with

MJ Users recruiting precuneus, postcentral gyrus, insula and anterior cingulate cortex,

and striatum to a greater degree than Controls. This relative reduced activity of Controls

may reflect their improved performance over the course of the task, selecting more often

from advantageous decks as the task continues. This suggests that they have “solved” the

task. This phase of the task, therefore, becomes less effortful for Controls over time.

Direct group comparisons between groups of activity during selection revealed that MJ

Users, compared to Controls, had greater activity in the insula and cingulate cortex. Both

67

of these areas have been hypothesized to represent changes in emotional/somatic states

during IGT performance (Li et al., 2010), suggesting that MJ Users may experience

altered affective processing while implementing behavioral decisions. MJ Users also had

greater activity in the parahippocampal gyrus, suggesting potential differences in memory

processes. Disrupted memory processes are known to exist in MJ Users, and in the

context of the IGT, this may be associated with the failed integration initial task

instructions (to avoid the decks with the worst losses) and previous choice outcomes

(large monetary losses) while making selections. Furthermore, increased

parahippocampal activity and decreased frontocortical activity has been associated with

learning and memory deficits in marijuana users (Nestor et al. 2008). Finally, greater

activity in MJ Users, compared to Controls, in the right insula and somatosensory

association cortex during disadvantageous selections suggest that MJ Users may attend

more to disadvantageous decks during the selection component of the task (Chen et al.

2010).

During the evaluation component of the IGT, both Controls and MJ Users had more

robust activity than during the selection component. Similar to selection, this activity

was present in overlapping neural networks. Unlike selection, however, activity during

evaluation was more extensive in Controls. Both groups had greater activity in cerebellar

and primary visual cortex during evaluation, compared to control events, consistent with

greater visual demands during this component of the task. Compared to control events,

both groups had large clusters of greater activity in bilateral parietal-frontal attention and

working-memory networks known to activate during various cognitive tasks (Nagel et al.

2010; Szczepanski et al. 2010). While both groups had activity in the right DLPFC

68

consistent with its involvement in working memory (Hillary et al. 2006), only Controls

displayed activity in the left DLPFC. This may reflect an altered ability of MJ Users to

successfully process the amount of information received during evaluation, as decreased

functional connectivity of the left DLPFC in working memory networks has recently

been linked to poor cognitive load abilities (Nagel et al. 2010). Controls also displayed

increased activity in the anterior cingulate and medial prefrontal cortex during evaluation,

which is consistent with the role of these areas in integrating affective and cognitive

information and responding to subjective value (Sripada et al. 2010). Finally, Controls

had greater striatal activity during evaluation, compared to control events, whereas MJ

Users did not, suggesting potential differences in updating values of chosen actions (Kim

et al. 2009).

Direct comparisons between groups during evaluation revealed that Controls had greater

activity in the posterior cingulate cortex, compared to MJ Users. Furthermore, greater

activity in the posterior cingulate cortex, together with greater activity in the

somatosensory cortex, was coincident with the evaluation of monetary losses. This

further suggests that while evaluating the consequences of their choices, MJ Users had

decreased activity in brain areas known to be involved in attention and emotional/somatic

processing (Sripada et al. 2010). As information about monetary losses drives the

development of advantageous performance on the IGT, appropriate response to and

integration of this information is vital for good performance on the task.

There are limitations of the current study based on the task design and analysis that may

warrant further investigation. In this study all participants received the same monetary

compensation for completion, with the exception of the best performer who received a

69

fifty dollar bonus. It is unclear if performance in MJ Users would vary as a function of

motivation for various reward types (e.g. real versus fictitious money), which has been

observed in other populations (Bowman and Turnbull 2003; Vadhan et al. 2009). There

were more cigarette smokers in the MJ Users group than the Controls group. Analysis of

the smokers (n = 8) and non-smokers (n = 8) in the MJ User group however revealed no

difference in behavioral performance or brain activity, similar to other studies that have

reported no difference smokers and non-smokers (Harmsen et al. 2006; Lejuez et al.

2003). Furthermore, similar to other imaging studies with significant differences in

demographic variables between groups (Bolla et al. 2005), the number of cigarettes

smoked per day was incorporated as a covariate in the imaging analyses. Finally,

preexisting conditions including a family history of substance abuse or genetic

background, which were not examined in this study, may play a role these results.

In summary, the present data show that MJ Users perform poorly on a complex decision

making tasks, failing to alter their selection strategies over the course of the task,

continuing to make disadvantageous choices that result in monetary losses. This poor

performance was accompanied by as altered brain activity during both selection and

feedback phases of the task. Though demonstrating similarities in the networks utilized

during selection and evaluation, MJ Users had increased activity in brain areas associated

with attention, affective processing and memory while making choices on the task,

whereas Controls engaged a wider network during feedback and evaluation. These data

suggest that MJ Users fail to use feedback to develop alternative strategies to improve

performance. This may be due to reduced information about negative or aversive events

or the failure to use this information during the evaluation of their choices. A reduced

70

sensitivity to negative consequences such as poor job ratings or educational failures, as

well as negative social cues can impact performance in many aspects of life and could

have significant implications for the initiation and success of treatment.

71

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76

CHAPTER III

POOR DECISION-MAKING BY CHRONIC MARIJUANA USERS IS

ASSOCIATED WITH DECREASED FUNCTIONAL RESPONSIVENESS TO

NEGATIVE CONSEQUENCES

Michael J. Wesley a, Colleen A. Hanlon

a, Linda J. Porrino

a,b

a Department of Physiology and Pharmacology

b Center for the Neurobiological Investigation of Drug Abuse

Wake Forest University School of Medicine

Winston-Salem, NC 27157 (U.S.A.)

77

ABSTRACT

Chronic marijuana users (MJ Users) perform poorly on the Iowa Gambling Task (IGT), a

complex decision-making task in which monetary wins and losses guide strategy

development. This study sought to determine if the poor performance of MJ Users was

related to differences in brain activity while evaluating wins and losses during the

strategy development phase of the IGT. MJ Users (16) and Controls (16) performed a

modified IGT in an MRI scanner. Performance was tracked and functional activity in

response to early wins and losses was examined. While the MJ Users continued to

perform poorly at the end of the task, there was no difference in group performance

during the initial strategy development phase. During this phase, before the emergence

of behavioral differences, Controls exhibited significantly greater activity in response to

losses in the anterior cingulate cortex, medial frontal cortex, precuneus, superior parietal

lobe, occipital lobe and cerebellum as compared to MJ Users. Furthermore, in Controls,

but not MJ Users, the functional response to losses in the anterior cingulate cortex,

ventral medial prefrontal cortex and rostral prefrontal cortex positively correlated with

performance over time. These data suggest MJ Users are less sensitive to negative

feedback during strategy development.

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INTRODUCTION

Marijuana is the most commonly used illegal drug in the United States and is known to

influence multiple aspects of executive function including impulsivity (McDonald et al.,

2003), attention (Fletcher et al., 1996; Pope et al., 2001), working memory (Miller and

Branconnier 1983) and cognitive flexibility (Bolla et al., 2002; Lane et al., 2007).

Importantly, chronic marijuana use is associated with deficits in decision-making that

impair the ability to make advantageous decisions over time (Whitlow et al., 2004;

Hermann et al., 2009). While the basis for this deficit has not been completely

characterized, successful strategy development during normal decision-making involves

the processing of positive and negative information, and using this information to guide

future decisions towards achieving a goal (Sutton and Barto 1998; Dayan and Balleine

2002; Camerer 2003). Understanding the basis of poor strategy development in chronic

marijuana users (MJ Users), therefore, may help explain their deficits in decision-making.

The Iowa Gambling Task (IGT) is a complex decision-making task (Bechara et al., 1994)

that has been used to demonstrate deficits in current (Whitlow et al., 2004; Hermann et

al., 2009) and abstinent heavy marijuana users (Bolla et al., 2005). To perform the task,

participants begin selecting cards randomly from four card decks under ambiguous

conditions. Each selection produces a monetary gain or win; however, some selections

also result in a monetary loss. Over time, based on the wins and losses associated with

each deck, two of the decks emerge as advantageous and two disadvantageous. The

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disadvantageous decks produce larger immediate gains but larger losses over time while

the advantageous decks yield smaller immediate gains but smaller losses over time.

Performance on the IGT is typically measured by a Net Score which reflects the

difference between selections allocated to advantageous and disadvantageous decks

throughout the task. In the early phases of the task, as participants evaluate win and loss

contingencies associated with deck choices, they develop decision-making strategies that

are implemented in later phases of the task. In previous studies from our lab, no

differences in Net Score between groups were observed in the early strategy development

phase of the task as both healthy controls (Controls) and MJ Users chose predominantly

from disadvantageous decks (Whitlow et al., 2004). It was only after multiple exposures

to win and, in particular, loss evaluation that Controls generally began shifting their

selections to the smaller gain but smaller loss, advantageous decks. MJ Users, in

contrast, generally failed to alter their selection patterns and continued to select from

disadvantageous decks throughout the task. These data are consistent with other studies

that suggest that the evaluation of, and response to, monetary wins and losses are crucial

for the development of successful decision-making strategies and successful performance

as measured by higher Net Scores (Bechara et al., 1994; Bolla et al., 2005; Lawrence et

al., 2009). Therefore, the poor performance of MJ Users on this task suggests that their

processing of wins and losses during the initial phases of the task may differ significantly

from Controls, resulting in ineffective strategy development.

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Several imaging studies have focused on the roles of wins and losses on performance

throughout the IGT. In a cohort of healthy participants, comparing the evaluation of wins

and losses across the entire task revealed that monetary wins produced greater activity in

medial frontal brain areas whereas losses were associated with greater activity in lateral

frontal regions (Tanabe et al., 2007). Lin et al. (2008) observed that the largest monetary

losses on the task produced greater activity in medial frontal and parietal cortical regions,

when compared to control events. Interestingly, MJ Users exhibit altered patterns of

activation in many of these same brain regions while performing other executive function

tasks (Quickfall and Crockford 2006; Chang and Chronicle 2007). While these studies

demonstrate differences in the functional activity induced by wins and losses in healthy

Controls, there have been no studies to date comparing win and loss outcomes in MJ

Users who are known to perform poorly on the task, particularly during the critical

strategy development phase of the task as participants acquire the foundations of their

decision-making strategies. Previous imaging studies of MJ users during performance of

the IGT task have been limited to abstinent users in whom performance was assessed

with positron emission tomography which did not allow the distinction between various

phases of the task, nor the evaluation of wins and losses separately (Bolla et al., 2005).

The purpose of this study, therefore, was to 1) isolate functional brain activity in Controls

and MJ Users during win and loss evaluation in the strategy development phase of the

IGT before differences in behavioral performance emerge and to 2) examine functional

differences between groups during this sensitive period of the task. Finally, we sought to

identify activity during the strategy development phase that is predictive of learning on

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the task. We hypothesized that MJ Users would display altered brain activity in response

to evaluation during this strategy development phase of the task. Specifically, we

expected MJ Users to show decreased activity during the evaluation of monetary losses.

METHODS

Participants

Sixteen MJ Users and 16 age and gender matched non-marijuana smoking controls

(Controls) were included in this study (see Table 1). All participants were right-handed.

Following an initial phone screen, participants were invited into the laboratory and

agreed to participate in procedures approved by the Wake Forest University School of

Medicine Institutional Review Board. On the initial visit, participants provided urine

samples to test for pregnancy and drug use and were administered the Structured Clinical

Interview for DSM-IV (SCID; First 1997) as well as the Wechsler Abbreviated Scale of

Intelligence (WASI; Wechsler 1999). Exclusion criteria included systemic diseases of

the central nervous system, head trauma, neurological disorders, Axis-I psychiatric

disorders (other than marijuana dependence for the MJ Users), abuse of substances other

than nicotine, or an I.Q. of less than 80. MJ Users were required to test negative for illicit

drugs other than marijuana and Controls were required to test negative for all illicit drugs.

Participants who met all inclusion criteria were scheduled for a scan visit. MJ Users were

asked to abstain from using marijuana starting at midnight the night before the scheduled

scan visit.

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Procedure

On the scan visit, participants arrived at the testing center approximately three hours prior

to the acquisition of their functional MRI scans. Participants provided urine samples to

test for pregnancy and drug use and completed depression (Beck’s Depression Inventory)

and anxiety (Speilberger Test of Anxiety) inventories. At no time did MJ Users report or

overtly exhibit signs of marijuana withdrawal (Budney and Hughes 2006).

Approximately one hour before entering the scanner, participants were trained on the IGT

using a standard laptop computer and button box. As part of the training, participants

visually followed along as task instructions were read aloud by a study technician.

Participants then completed a trial run of the IGT containing 8 gambling events and 2

control events in order to become familiar with the layout and timing of the task. During

the trial run, monetary win and loss contingencies associated with deck selections were

randomized in order to avoid strategy carryover to the scanner task. Participants were

made aware of this distinction and performed an additional trial run if the initial run

produced multiple mistimed events. Participants were informed that the individual who

performed the best on the task would receive a fifty dollar bonus at the end of the study.

Although previous studies have found no difference between smokers and nonsmokers on

IGT performance (Lejuez et al., 2003; Harmsen et al., 2006), participants were given a 15

minute break with the opportunity to smoke a cigarette to avoid potential confounds of

nicotine withdrawal on functional brain activity (Wang et al., 2007; Xu et al., 2007).

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Three participants in the MJ Users group took advantage of the opportunity to smoke a

cigarette.

Iowa gambling task (IGT)

In the MRI scanner a modified version of the IGT was presented on MR compatible

goggles, and responses were recorded on a button box positioned under the right hand.

Before the task onset participants followed along as the task instructions were read aloud.

As part of the instructions, emphasis was placed on the key role that monetary losses play

in solving the task. The instructions were read as follows:

In front of you are four decks of cards: A, B, C, and D. When the game begins,

you will see instructions to "Select a Deck..." for each turn. During each turn you

have about 2 seconds to choose one card from any deck. You are free to switch

from one deck to any other as often as you wish. Turns will last for varying

lengths of time, so don't be concerned if you do not receive instructions

immediately following a card choice.

Each time you select a card you will win some money. Every so often, however,

you will also lose some money. The goal of the game is to win as much money as

possible and to avoid losing money. There is no way to figure out when you will

lose money. All I can say is that some decks are worse than others. No matter how

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much you find yourself losing, you can still win the most if you stay away from

the worst decks.

Occasionally, you will be prompted to select from a specific deck but you will

neither win nor lose any money for that turn. Please treat the play money in this

game as real money and any decision on what to do with it should be made as if

you were using your own money. You will not know when the game will end.

Please keep on playing until you are told to stop.

Once participants verbally acknowledged comprehension of the instructions the task was

initiated with a 20 second countdown. Each participant performed three segments or runs

of the IGT each consisting of 45 gambling events and 13 randomly inserted control

events. The first run of the task was considered the early, strategy development phase

and consisted of events 1 through 58. The second run consisted of events 59 through

116, and the third run encompassed events 117 through 174.

When the task started participants received instructions to “Select a Deck” for a fixed

period of 2 seconds via a text box on the upper left-hand side of the task screen. During

this period participants selected a card from one of four card decks labeled A, B, C and D

by pressing buttons 1, 2, 3 or 4 on the button box, respectively. Immediately following

the selection period, participants received feedback for a variable period of time jittered

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around 2 seconds. The lengths of evaluation events were jittered to correct for stimulus-

onset asynchrony and to assure adequate sampling of the hemodynamic response for

imaging analyses. The end of each evaluation period signaled the onset of the next

selection period. During evaluation, participants viewed the monetary gain and/or loss

associated with their selection. Evaluation information alternated with task instructions

in the text box on the upper left-hand side of the screen. On the upper right-hand side of

the screen an ongoing monetary score was updated following each deck selection. On

selection control trials participants received instructions to select a card from a specific

deck (e.g. “Select Deck B”). Directly following selection control events, participants

viewed an evaluation control screen that contained the phrase “You Neither Win Nor

Lose”. For all imaging analyses win and loss evaluation events were compared to

evaluation control events.

Selections from decks A and B resulted in immediate gains of $100 each with losses over

time ranging from $250 to $1200 and were considered disadvantageous. Selections from

decks C and D produced immediate gains of $50 with losses over time ranging from $50

to $250 and were considered advantageous. To ensure that the typical advantageous

decision-making emerged in the control group following the strategy development phase

of the task (RUN 1), the proportion of responses allocated to disadvantageous (A and B)

and advantageous (C and D) decks were calculated for each of the three sections of the

task (RUN 1, RUN 2 and RUN 3) and used to calculate individual Net Scores of

performance.

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Functional MRI data acquisition

Images were acquired on a 1.5T General Electric scanner with a birdcage-type standard

quadrature head coil and an advanced nuclear magnetic resonance echoplanar system.

Foam padding was used to limit head motion. High-resolution T1-weighted anatomical

images (3D SPGR, TR=10 ms, TE=3 ms, voxel dimensions 1.0×1.0×1.5 mm, 256×256

voxels, 124 slices) were acquired for co-registration and normalization of functional

images. A total of 162 co-planar functional images were acquired using a gradient

echoplanar sequence (TR=2100 ms, TE=40 ms, voxel dimensions 3.75×3.75×5.0 mm,

64×64 voxels, 28 slices). The scanning planes were oriented parallel to the anterior–

posterior commissure line and extended from the superior extent of motor cortex to the

base of the cerebellum. Six volumes of data were acquired during the 20 second

countdown period and immediately discarded to allow for equilibrium before selections

began.

Statistical analyses

Demographics and behavior

Independent samples t-tests were used to compare groups on parametric demographic

variables. Chi-square analyses were used to compare group differences in sex and the

proportion of participants who were cigarette smokers. To examine advantageous and

disadvantageous choices in each of the three sections (i.e. RUNS) of the task, a Net Score

of performance was calculated for each individual in each section by subtracting the

number of selections on advantageous decks from the number on disadvantageous decks.

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A positive Net Score reflected more advantageous deck selections, relative to

disadvantageous selections, within that section of the task. To determine if the Net Score

varied according to group a 2x3 mixed model ANOVA was conducted with between-

subjects group factor (Controls and MJ Users) and within-subject RUN factor (1, 2 and

3). Independent and paired samples t-tests were used for post-hoc analyses accordingly

with Bonferroni corrections. To determine if the number of event types experienced

differed between groups during the strategy development phase of the task (RUN 1), a

2x2 ANOVA was conducted with between-subjects group factor (Controls and MJ Users)

and within-subjects event type (win and loss evaluation). To examine improvement in

task performance across the task, a Net Score Difference value was calculated for each

individual. This value was calculated by subtracting each individual’s Net Score in the

first section of the task from their Net Score in the last section of the task (Net Score

Difference = RUN 3 Net Score minus RUN 1 Net Score). A positive Net Score

Difference reflected improvement in performance across the task. An independent

samples t-test was used to examine differences in Net Score Difference values between

groups. All behavioral data were analyzed using the Statistical Package for the Social

Sciences (SPSS) version 11.5.

Functional MRI preprocessing and data analysis

To examine differences in the functional brain response to win and loss evaluation during

the strategy development phase (RUN 1), each individual’s neural response to win

evaluation, loss evaluation, and control evaluation events was isolated. The functional

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data from each participant were corrected for acquisition time (slice timing), realigned to

the first volume (motion correction), normalized into a standardized neuroanatomical

space (Montreal Neurological Institute brain template), smoothed using a Gaussian kernel

of 8 mm, and high-pass filtered (128s) to remove low frequency noise. Inspection of

motion correction revealed that all corrections were less than 2 mm. For each individual,

a multiple linear regression analysis was performed. Regressors corresponded to time

periods during which the participant 1) made deck selections or 2) viewed selection

control events and then evaluated feedback of monetary 3) wins, 4) losses or 5) viewed

evaluation control events. As the aim of this study was to investigate the neural

responses during the evaluation of wins and losses, event times corresponding to

selection events and selection control events were modeled to remove variance associated

with periods but not included in further contrast maps. Evaluation conditions (win, loss

and control) were modeled by convolving relevant evaluation times with a canonical

hemodynamic response function. Trials in which no response was made were excluded

from the analyses. For cigarette smokers, the reported average number of cigarettes

smoked per day was treated as a nuisance variable and variance associated with this

variable was covaried out of all functional imaging analyses, an approach used in other

substance abuse studies (Bolla et al., 2005). First, for each individual, statistical contrast

maps of activity associated with all RUN 1 evaluation periods were made by comparing

activity during all RUN 1 evaluation events to activity during all RUN 1 evaluation

control events (win evaluation + loss evaluation > control evaluation). Next, for each

individual, statistical contrast maps of RUN 1 wins (win evaluation > control evaluation)

and RUN 1 losses (loss evaluation > control evaluation) were created. These contrast

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maps were compared between groups to find 1) differences between groups in all RUN 1

evaluation and 2) differences between groups specific to win and loss evaluation. For

these between-group comparisons, contrast maps were thresholded with a voxel-wise P

value of 0.05 further adjusted at the cluster level (P <0.001, corrected) to reduce the

chance of Type I error (Christakou et al., 2009; Hartstra et al., 2010). Lastly, in order to

identify the relationship between RUN 1 evaluation event types (wins or losses) and

performance on the IGT, within each group, the Net Score Difference measure (a

measure of improvement across the task) was regressed with the whole brain functional

response during RUN 1 win (win evaluation > control evaluation) and loss (loss

evaluation > control evaluation) events. For these analyses, a voxel-wise P value of 0.01

was used with further adjustments at the cluster level (P <0.001, corrected). All imaging

analyses were performed with SPM 5 (Wellcome Department of Imaging Neuroscience,

London, UK) in the MATLAB 7.0 (Mathworks, Natick, MA) shell using an event-related

model (Friston et al., 1998).

RESULTS

Demographics

A description of study participants is shown in Table 1. MJ Users were 26.4 ± 3.6 years

old (mean ± sd) and reported using marijuana 2.1 ± 1.5 times a day, 29.4 ± 1.0 days a

month, for 9.6 ± 4.1 years. The average age of first marijuana use was 16.3 ± 2.1 years.

Controls were 26.6 ± 6.1 years old and did not meet dependence criteria for any illegal

drugs. Four of 16 members of the Control group reported previous marijuana use with

use limited to fewer than 50 lifetime uses, occurring more than 2 years prior to the study.

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Four of 16 MJ Users met criteria for marijuana dependence. On the scanning day, all

participants had negative urine screens for illegal substances (other than marijuana in MJ

Users). All members of the MJ Users group tested positive for marijuana metabolites the

day of scanning and reported a mean (± sd) abstinence from marijuana of 12.0 ± 2.9

hours (range = 8.5 – 16 hours). There were no significant differences between groups in

depression scores on the Becks Depression Inventory, nor anxiety scores on the

Spielberger Test of Anxiety at the time of scanning.

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IGT behavioral performance

Behavioral performance on the IGT is shown in Fig. 1a. In Controls, behavioral

performance improved across the three segments of the task, observed as a shift in the

mean (± sd) Net Score (RUN 1 = −5.50 ± 14.2, RUN 2 = +3.06 ± 16.1, RUN 3 = +5.06 ±

20.2). Improvement was not observed in MJ Users (RUN 1 = −7.25 ± 7.3, RUN 2 =

−3.38 ± 8.7, RUN 3 = −8.5 ± 11.4). There was a significant group x run interaction

Table 1. Group Demographics

Variable

Controls

(N = 16)

MJ Users

(N = 16)

Mean (± SD) Mean (± SD)t / X2

Valuep

Age (years) 26.6 (6.1) 26.4 (3.6) 0.11 n.s.

I.Q. 115 (8.4) 109.1 (13.6) 1.47 n.s.

Sex 0.29 n.s.

Male 6 9

Female 10 7

Cigarette Smokers 12.5 % 50 % 5.24 0.022

Alcohol AUDIT Score 2.8 (2.0) 4.2 (2.3) 1.91 n.s.

Caffeine (mg/day) 96.9 (99.8) 120.3 (119.5) 0.60 n.s.

Spielberger State Anxiety 24.6 (5.6) 26.1 (6.6) 0.69 n.s.

Beck’s Depression 2.5 (4.0) 3.4 (3.5) 0.71 n.s.

Marijuana Use:

Age of onset (years) 16.3 (2.1)

Years of Total Use 9.6 (4.1)

Days per month 29.4 (1.0)

Times per day 2.1 (1.5)

Years at current use level 4.5 (3.8)

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F(1,30) = 4.31, p = 0.04). Post hoc analysis revealed that during the final segment of the

task Controls had significantly higher Net Scores as compared to MJ Users (RUN 3:

Controls = +5.0 ± 14.2 vs. MJ Users = −8.5 ± 11.4) t(16) = 2.90, p = 0.01.

During the first segment of the task, the strategy development phase, groups did not differ

in performance as measured by the Net Score (RUN 1: Controls = −5.50 ± 14.2 vs. MJ

Users = −7.25 ± 7.3). During this segment, a significant group x event type interaction

was observed F(1,30) = 7.73, p = 0.009. As shown in Fig 1b, there was no difference in

the number of win events experienced between groups (Wins: Controls = 34.19 ± 1.1 vs.

MJ Users = 33.13 ± 2.2). There was, however, a significant difference in the number of

loss events experienced between groups, with MJ Users experiencing more loss events

(11.50 ± 2.3), compared to Controls (9.13 ± 1.8) t(30) = 3.28, p = 0.003.

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Figure 1. a) Behavioral Performance in three

sections of the Iowa Gambling Task (174 trials). By

the end of the task (RUN 3), Controls (unfilled)

made significantly more advantageous deck

selections compared to chronic marijuana users (MJ

Users; filled). There was no difference in deck

selections between groups during the strategy

development phase (RUN 1). b) During the strategy

development phase (RUN 1), MJ Users experienced

more loss events than Controls. * = p < 0.05

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Functional imaging during strategy development

Combined evaluation events (wins and losses)

In order to elucidate the role of early evaluation events during strategy development,

functional imaging analyses were restricted to the first segment of the task (i.e. RUN 1)

before performance differences emerged between the two groups. Analyses were also

restricted to the evaluation periods of this early phase as participants viewed feedback

regarding the positive (wins) and negative (losses) consequences associated with their

initial choices. Differences in brain activity between groups in response to all RUN 1

evaluation (win + loss) events can be seen in Fig. 2 and Table 2. Comparisons of activity

between groups revealed that, compared to MJ Users, Controls had significantly greater

activity during RUN 1 evaluation in several frontal brain regions. Controls displayed

greater responses in clusters of activity coincident with the anterior cingulate cortex (Fig

2a, b[Y = 26, 34, 38]) and medial frontal cortex (Fig 2a, b[Y = 54, 56]). This activity

also extended dorsally to include portions of the superior medial frontal cortex (Fig 2a,

b[Y = 34, 38]). In contrast, there were no suprathreshold clusters where MJ Users had

greater activity than Controls during RUN 1 evaluation (Table 2).

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Differences according to evaluation event type (wins or losses)

Differences in activity between Controls and MJ Users was further examined according

to the specific evaluation event type: win evaluation (win events > control events) and

loss evaluation (loss events > control events). These data can be seen in Fig. 3 and Table

2. There were no differences between groups in the activity observed during win

evaluation. There were several areas, however, where activity during loss evaluation was

greater in Controls, compared to MJ Users (Fig. 3). Portions of this activity were

spatially coincident with differences observed when examining combined event types

(wins + losses). For example, during loss evaluation, Controls displayed greater activity

Figure 2. The difference in functional activity of Controls and chronic marijuana users

(MJ Users) during all evaluation events (wins + losses) during the strategy development

phase of the IGT (RUN 1). Images show clusters where activity was greater in Controls

compared to MJ Users. There were no suprathreshold clusters where MJ Users had

greater activity compared to Controls. Probability thresholds were set to p< 0.05 at the

voxel-level and further corrected at the cluster level (p<.001,corrected).

Y = 56543826 34

X = 10

All Evaluation:

Controls > MJ Users

2

6

t scores

a

bR

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than MJ Users in the anterior cingulate cortex (Fig 3a, b[Y = 26, 34, 38]) and medial

frontal cortex (Fig 3a, b[Y = 56, 54]). This activity also extended dorsally into the

superior medial frontal cortex (Fig 3a, b[Y = 34, 38]) but also extended rostrally to

include more prefrontal areas (Fig 3a, b[Y = 54, 56]). In addition, differences emerged

that were not observed in the combined analysis. Compared to MJ Users, Controls

showed greater activity during loss evaluation in clusters enveloping portions of the

precuneus, posterior cingulate cortex and dorsal cerebellum as well as portions of the

superior parietal lobe and occipital cortex (Fig. 3a; Table 2.).

Figure 3. The difference in functional activity of Controls and chronic marijuana users

(MJ Users) during monetary loss evaluation in the strategy development phase of the

IGT (RUN 1). Images show clusters where activity was greater in Controls compared to

MJ Users. There were no suprathreshold clusters where MJ Users had greater activity

compared to Controls. There were also no differences between groups during win

evaluation in this phase of the task. Probability thresholds were set to p< 0.05 at the

voxel-level and further corrected at the cluster level (p<.001,corrected).

Y = 56543826 34

Loss Evaluation:

Controls > MJ Users

2

6

t scores

bR

X = - 6

a

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Loss evaluation and task performance

To characterize the relationship between early evaluation events and IGT performance,

the Net Score Difference (Net Score: RUN 3 - RUN 1), a measure of improvement over

the course of the task, was correlated with the functional response to RUN 1 win and loss

evaluation events within each group. Consistent with the Net Score results (Fig. 1a),

groups significantly differed in the Net Score Difference t(30) = 2.08, p = 0.04 (Fig. 4a).

Table 2. Clusters of significant differences in BOLD signals during Win and Loss evaluation between Controls and MJ Users

during strategy development (Run1) on the IGT

Analysis Side Anatomical Regions BA*MNI Coordinates+

(x, y, z)

Maximum

Voxel

t-value

All Evaluation (Wins + Losses)

Controls > MJ Users RAnterior Cingulate

Cortex24 6 26 22 4.53

R Medial Frontal Gyrus 10 16 54 6 4.35

MJ Users > Controls N/A no suprathreshold cluster

Win Evaluation

Controls > MJ Users N/A no suprathreshold cluster

MJ Users > Controls N/A no suprathreshold cluster

Loss Evaluation

Controls > MJ Users RAnterior Cingulate

Cortex24 6 26 24 4.20

L Medial Frontal Gyrus 9 - 12 38 34 3.92

R Medial Frontal Gyrus 8 8 34 46 3.71

R Precuneus 7 4 - 60 64 3.95

L Cerebellum: Declive - 28 - 74 - 22 3.93

R Superior Parietal Lobe 7 20 - 72 56 3.98

MJ Users > Controls N/A no suprathreshold cluster

*BA, Brodmann areas. Listed areas correspond to location of the maximum voxel of activation and other BAs associated with the

activity cluster. +MNI, Montreal Neurological Institute. Bolded regions and coordinates correspond to the maximum voxel of

activity within the cluster followed by regions of local maxima within the cluster.

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The mean (± sd) Net Score Difference in Controls (10.56 ± 19.5; range: −19 to +46) was

significantly greater than that observed in MJ Users (−1.25 ± 11.8; range: −26 to +20). A

regression of the Net Score Difference with the whole brain response during RUN 1 loss

evaluation revealed activity in Controls that was associated with future improvement in

task performance. In Controls, but not MJ Users, the magnitude of response in the

anterior cingulate cortex, ventral medial prefrontal cortex, and rostral prefrontal cortex

during RUN 1 loss evaluation positively correlated with the Net Score Difference (Fig

4b). No relationship was observed between Net Score Difference and the response to

RUN 1 win evaluation in either group. Together these data demonstrate that before

behavioral differences emerge on the IGT, MJ Users have decreased responsivity to the

monetary losses that aid strategy development.

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DISCUSSION

The results of the present study demonstrate that chronic marijuana users (MJ Users)

perform poorly on the Iowa Gambling Task (IGT), failing to develop advantageous

decision making strategies, thus confirming previous studies using similar tasks (Whitlow

et al., 2004; Hermann et al., 2009). Furthermore, the present study extended these

findings by showing that during the strategy development phase of the IGT, before the

emergence of group differences in behavioral performance, the functional brain activity

Figure 4. a) Improvement in performance across the Iowa Gambling Task (IGT) as

measured by the Net Score Difference (Net Score: RUN 3 – RUN 1). Performance

in Controls significantly improved over the course of the task, compared to chronic

marijuana users (MJ Users). b) Net Score Difference correlated with whole brain

functional activity during RUN 1 loss evaluation in Controls and MJ Users.

Responses in the anterior cingulate cortex, ventral medial prefrontal cortex and

rostral prefrontal cortex during RUN 1 loss evaluation predicted improvement in

Controls, but not MJ Users. * = p < 0.05

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of MJ Users during evaluation is distinctly different from that of healthy controls

(Controls). During early evaluation events (wins + losses) MJ Users had smaller BOLD

responses than Controls in the anterior cingulate cortex, the ventral medial prefrontal

cortex and portions of the superior medial frontal cortex. During the evaluation of

monetary losses, MJ Users had less activity in these same areas as well as in the

precuneus, posterior cingulate cortex, the superior parietal lobe, and portions of the dorsal

cerebellum and occipital cortex, compared to Controls. Finally, correlating performance

over the course of the task with BOLD activity during early loss evaluation revealed that

the response to losses in the anterior cingulate cortex, ventral medial prefrontal cortex

and rostral prefrontal cortex predicted improvement in Controls, whereas MJ Users

showed no correlations. These data suggest that the failure of MJ Users to develop

successful decision-making results from a relative insensitivity to the early monetary

losses that aid strategy development and precede successful performance on the IGT.

Previous studies of healthy participants have shown that in the course of strategy

development, positive and negative information guide future decisions towards achieving

a goal (Sutton and Barto 1998; Dayan and Balleine 2002; Camerer 2003). This is also

the case of the IGT (Bechara et al., 2005), where early monetary losses provide the

incentive to shift to advantageous deck selections over time and early wins encourage

continued selection on disadvantageous decks. Previously it was found that over the

course of the task, activity in the inferior parietal lobe and medial frontal cortex is

increased during evaluation, and the medial prefrontal cortex responds to the largest

losses (Lin et al., 2008). Our data support these findings and extend them by highlighting

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the importance of the medial frontal cortex while processing early losses during the

strategy development phase of the task. Furthermore, our data show that, relative to

Controls, MJ Users have altered processing in this brain area.

The separation of all of the evaluation events (wins and losses) into specific event types

(wins or losses) revealed that the response to monetary losses was critical for the

differences in strategy development between Controls and MJ Users. This is supported

by the absence of differences between groups in the functional response to early win

events (Table 2). Furthermore, the differences that emerged during loss evaluation,

particularly in the medial frontal lobe, were spatially coincident with the differences

observed when all evaluation events, wins and losses, were considered together.

Therefore, it appears that the key difference between MJ Users and Controls is in the

evaluation of the negative information conveyed by losses, rather than in the evaluation

of wins.

Reduced responses in the superior frontal gyrus during loss evaluation is consistent with

previous reports examining evaluation of losses over the entire course of the task (Tanabe

et al., 2007) . This compromised activity in MJ Users, as well as that observed in the

precuneus and cingulate cortex, may represent altered attentional resources directed to

monetary losses, similar to functional abnormalities observed in adults with attention-

deficit disorder (Castellanos et al., 2008). Also, the functional relationship between the

medial superior frontal gyrus and the precuneus has been shown to be time-locked to

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attentional shifts between object features (Nagahama et al., 1999), suggesting that MJ

Users may have an inability to shift attention between the various components of trials

(i.e. selection vs. evaluation). This could also be the case for win events. However,

during loss evaluation the additional information regarding the monetary loss represents

an additional and necessary component that must be attended to in order to learn the task.

It is possible that the lack of activity in these areas in MJ Users is associated with failure

to attend to this additional component. Failure to addend to this information would

explain continued selections made on disadvantageous decks.

There are several potential explanations for diminished activity of MJ Users in the

anterior cingulate cortex and ventral medial prefrontal cortex, as compared to Controls.

This could reflect compromised error processing during loss evaluation events, a

hypothesis suggested by others (Lin et al., 2008) and consistent with studies

demonstrating that anterior cingulate activity increases in response to errors (Swick and

Turken 2002). This may also reflect more general deficits in performance monitoring, as

increases have also been observed under conditions where errors are likely to occur

(Carter et al., 1998) and during violations of outcome expectancy (Oliveira et al., 2007).

These reductions may reflect decreased motivation as a result of experiencing monetary

losses (Martin-Soelch et al., 2009; Simoes-Franklin et al., 2009). Differences in

motivation did not appear to be a significant contributing factor in the study, however, as

all individuals completed the task, and groups did not differ in the number of omitted or

“no response” events on the task. More importantly, on selection trials immediately

following monetary loss events, both groups shifted selections away from loss producing

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decks more than 95% of the time. This suggests that monetary losses motivated changes

in selection strategies in both groups. Finally, it has been hypothesized that lesions to

these medial brain areas results in the inability to integrate affective information into

executive functioning processes (Bechara 2004). This suggests that MJ Users may fail to

incorporate the negative affective experience of early monetary losses into developing

strategies to perform the task. While further studies are needed to explore these

possibilities, it is clear from this study that MJ Users lack a functional response to early

losses in the anterior cingulate, the ventral medial prefrontal cortex, and rostral prefrontal

cortex that predicts improvement in Controls. This is evidence that altered processing to

early losses in these areas is directly related to the inability of MJ Users to develop

advantageous strategies on the task.

There are a growing number of reports that demonstrate abnormalities in affective

processing in recreational cannabis users as well as long-term heavy marijuana users

(Degenhardt et al., 2003; Wadsworth et al., 2006; Dorard et al., 2008; Skosnik et al.,

2008; Gruber et al., 2009). For example, Gruber et al. (2009) recently demonstrated that

MJ Users have altered responsivity to affective faces presented below the level of

consciousness in the amygdala and the anterior cingulate cortex. The data from the

current study extend these findings by showing deficits specific to negative affective

information processing in MJ Users. This is consistent with the ability of cannabinoids to

modulate the behavioral responses to aversive stimuli and the conditional associations

between aversive events and the environment. For example, blocking normal or

endogenous cannabinoid system function compromises learned escape behaviors (Varvel

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et al., 2005). Furthermore, blocking the cannabinoid system genetically (CB1-deficient

mice) or pharmacologically (with a CB1 antagonist) increases the retention of fear

memories in altered mice, compared to wild type mice (Marsicano et al., 2002).

Conversely, and of particular relevance to this study, enhancing cannabinoid system

function with CB1 receptor agonists, similar to THC, blocks the expression of fear

memories as measured by fear-potentiated startle (Lin et al., 2006). These findings

suggest that the functional insensitivity to aversive events observed in the current study

may result from disrupted cannabinoid system function associated with heavy marijuana

use. This is also consistent with human studies demonstrating that THC administration

decreases the functional reactivity to social signals of threat in recreational users (Phan et

al., 2008).

That MJ Users appear to have a blunted response to negative stimuli is consistent with

studies demonstrating that the poor performance of cocaine users performing the IGT is

related to less responsiveness to losses (Stout et al., 2004). Other studies have observed

poor performance on the IGT in various drug abusing populations, however, these studies

did not evaluate the specific role that early win and loss evaluation play in development

of task performance (Bolla et al., 2003; Tucker et al., 2004; Vadhan et al., 2007; Acheson

et al., 2009; Vadhan et al., 2009). Results from the present study suggest that MJ Users

may be relatively insensitive to negative information as they first attempt to solve

problems. This insensitivity may interfere with the important role that monetary losses

play in facilitating successful strategy-development in the early phases of the IGT. As

the aim of this study was to model experiences in day-to-day marijuana users, we tested

105

individuals at a time between the offset of THC’s psychoactive effects (2-3 hrs) and the

onset of marijuana withdrawal symptoms (1-3 days). It is not clear if the results observed

here persist following prolonged abstinence, however, one study reported that behavioral

and neurofunctional changes in MJ Users performing the IGT can persist following 28

days of abstinence (Bolla et al., 2005).

The current study has some inherent limitations based on the task design and analysis that

may warrant further investigation. All participants received the same monetary

compensation for completion, with the exception of the best performer who received a

fifty dollar bonus. Though participants were asked to “treat the play money in this game

as real money” it is unclear if performance in MJ Users would vary as a function of

motivation for various reward types while performing the task (e.g. the IGT monetary

score reflecting real vs. fictitious money), which has been observed in both control

populations (Bowman and Turnbull 2003) and cocaine using populations (Vadhan et al.,

2009). There were more cigarette smokers in the MJ Users group than the Controls

group. Post-hoc analysis of the smokers (n = 8) and non-smokers (n = 8) in the MJ User

group, however, revealed no difference in behavioral performance or brain activity

during the task. This is congruent with other studies that have reported no difference in

IGT performance in smokers and non-smokers (Lejuez et al., 2003; Harmsen et al.,

2006). Furthermore, the number of cigarettes smoked per day was incorporated as a

covariate in the imaging analyses similar to other imaging studies with significant

differences in demographic variables between groups (Bolla et al., 2005). Finally, it is

not possible to determine whether these results are the direct result of a history of heavy

106

marijuana use or are the result of pre-existing conditions including psychiatric disorders

or genetic background.

To summarize, in the current study MJ Users failed to develop successful decision-

making strategies on the IGT, relative to Controls. During the early, strategy

development phase of the task, when performance did not differ between groups, MJ

Users processed evaluation differently than Controls. Specifically, MJ Users showed

reduced activity while evaluating monetary losses. Furthermore, MJ Users lacked a

functional response to monetary losses in medial frontal brain areas that predicted task

improvement in Controls. Since the early monetary losses on the IGT drives successful

strategy development, the diminished response to losses in MJ Users may explain their

inability to engage successful decision-making strategies on the task. These data suggest

that MJ Users do not process negative information in the same manner as non-marijuana

using Controls during ongoing decision-making. This may result in inefficient strategies

used to solve problems. In light of the growing number of people reporting marijuana

use disorder (Compton et al., 2004) an appreciation of the relationship between affective

information processing and decision-making in chronic marijuana users may be clinically

relevant. Understanding how marijuana influences the perception of what is “negative”

may help explain continued marijuana use and aid in the development of effective

strategies for the treatment of this disorder.

107

ACKNOWLEDGEMENTS

This work was supported by the National Institute of Drug Abuse grants DA007246

(MJW), DA020074 (LJP), and DA06634 (LJP). The authors thank, Mack D. Miller,

Hilary R. Smith and Thomas J.R. Beveridge for their comments on this manuscript and

Marla Torrence for her assistance in recruitment and processing of the participants. None

of the authors have any financial conflict of interest in the performance or publication of

this research.

108

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CHAPTER IV

ALTERATIONS IN FUNCTIONAL PROCESSING OF STIMULI JUDGED TO

BE EMOTIONAL IN CHRONIC MARIJUANA USERS

Michael J. Wesley1, Colleen A. Hanlon

1, Linda J. Porrino

1,2

1 Department of Physiology and Pharmacology

2 Center for the Neurobiological Investigation of Drug Abuse

Wake Forest University School of Medicine

Winston-Salem, NC 27157 (U.S.A.)

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ABSTRACT

The main psychoactive cannabinoid in marijuana, Δ9-tetrahydrocannabinol (THC),

attenuates functional brain responses to negative stimuli in recreational marijuana users

(Phan et al. 2008). Long-term heavy marijuana users (MJ Users) have decreased

functional responsiveness to emotional stimuli presented below the level of

consciousness (Gruber et al. 2009). The current study sought to identify if MJ Users

have decreased responsiveness for stimuli consciously judged as emotional. MJ Users

(17) and Controls (16) processed and judged stimuli ranging in emotional content while

in the MRI scanner. Performance was tracked and functional activity to emotional and

non-emotional stimuli was isolated. There was no difference between groups in

emotional judgments. Emotional stimuli, however, did not evoke a functional response

in the right amygdala and right middle frontal cortex of MJ Users that was observed in

Controls. In response to emotional stimuli, MJ Users had hypoactive responses in the

medial prefrontal cortex and anterior cingulate cortex, and this activity was significantly

less than Controls. Finally, MJ Users had decreased response magnitudes elicited by

positive stimuli in the left amygdala, bilateral insula and bilateral medial prefrontal

cortex, including the anterior cingulate cortex, compared to Controls. These data suggest

that although MJ Users can correctly recognize emotional content, they are functionally

insensitive to this information in brain areas associated with emotional/somatic and/or

cognitive judgments.

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INTRODUCTION

In recent years, both recreational and medicinal marijuana use has increased, and the

perception that marijuana use is harmful has decreased (Johnston et al. 2009).

Furthermore, there have been concurrent increases in both the levels of Δ9-

tetrahydrocannabinol (THC), the main psychoactive ingredient, found in marijuana

(Licata et al. 2005) as well as user preference to consume high potency marijuana (Chait

and Burke 1994). Cannabinoids, like those found in marijuana, have been shown to

modulate affective or emotional information processing in the brain (Fusar-Poli et al.

2009a; Fusar-Poli et al. 2009b; Griebel et al. 2005; Gruber et al. 2009; Laviolette and

Grace 2006; Lin et al. 2006; Marsicano et al. 2002; Moreira and Lutz 2008; Phan et al.

2008). With the observed increases in marijuana use, together with data demonstrating

the ability of cannabinoids to modulate affective processing, there has been increased

interest in understanding if emotional processing is altered in long-term marijuana users

(MJ Users). In a recent study addressing this issue, Gruber et. al. (2009) reported that MJ

Users have decreased brain activity to emotional stimuli presented below the level of

consciousness. It is not clear, however, if similar deficits exist for emotional stimuli that

are consciously considered to have emotional value. The purpose of this study, therefore,

was to use fMRI to examine brain activity of MJ Users and non-marijuana using controls

(Controls) as they viewed emotional stimuli individually judged as having emotional

content.

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Much of the research investigating the ability of cannabinoids to alter emotional

processing has focused on effects within the amygdala, a brain area known to responds

quickly to emotional stimuli (Davis and Whalen 2001; Phelps and LeDoux 2005). The

ability of cannabinoids to alter activity in the amygdala is consistent with the high

concentration of cannabinoid receptors in this brain area (Glass et al. 1997). In animals

cannabinoids have been shown to directly alter the processing of negative or aversive

information. For example, acute administration of cannabinoid agonists like THC into

the amygdala can extinguish fear memories (Marsicano et al. 2002) and prevent their

reconsolidation (Lin et al. 2006). This is consistent with data from human imaging

studies demonstrating that the administration of THC in recreational marijuana users

decreases amygdala responsivity to angry faces considered to be social signals of threat

(Phan et al. 2008). Similar to these data, MJ Users have been shown to have decreased

amygdala, as well as cingulate cortex, activity to masked emotional faces, compared to

Controls (Gruber et al. 2009). In this study, decreased activity was observed for both

positive and negative stimuli (happy and angry faces).

Like the amygdala, several other brain areas with large concentrations of cannabinoid

receptors have been shown to be involved in emotional processing. The insular cortex,

for example, has been implicated in processing somatic/emotional brain states (Craig

2009; Stein et al. 2007) and activity in the insula increases during emotional tasks in

individuals with emotional susceptibility (Iaria et al. 2008). The medial prefrontal cortex,

including the anterior cingulate cortex and the ventral medial prefrontal cortex have also

been shown to respond to emotional information (Hariri et al. 2003). These areas are

120

anatomically linked to the amygdala and insula and serve as an interface between

emotional and cognitive processing streams in the brain (Bechara et al. 2000; Bechara et

al. 2001; Chambers et al. 2006; Johnson et al. 2008). In MJ Users, activity in the medial

prefrontal cortex has been shown to be altered while performing complex decision-

making tasks that rely on affective processing for successful performance (Bolla et al.

2005; Wesley et al. in press). Given their demonstrated role in emotional information

processing, and their large concentration of cannabinoid receptors, it is possible that these

areas experience altered processing in MJ Users while viewing stimuli judged as having

emotional content.

In this study, to examine the possibility that conscious emotional processing is altered in

MJ Users, we measured the blood oxygen level dependent (BOLD) signal as Controls

and MJ Users viewed emotional photographs obtained from the International Affective

Picture System (IAPS) data base (Lang et al. 2005). Participants first viewed these

stimuli and then judge them as having positive (POS), little to no (NEU), or negative

(NEG) emotional content. Functional activity during the viewing period was isolated

according to each individual’s judgments. In an effort to thoroughly characterize

emotional processing, two analyses were performed. First, a conservative whole brain

analysis was performed to identify activity associated with viewing emotional stimuli

(POS + NEG > NEU). Next, an a priori analysis was performed examining the

magnitude of functional responses for each stimulus type in anatomical regions of interest

(ROIs) known to be involved in emotional processing. These ROIs included the 1)

amygdala, 2) insula 3) medial prefrontal cortex, including the anterior cingulate cortex, as

121

well as the 4) ventral medial frontal cortex, including the orbital frontal cortex, and 5)

rostral prefrontal cortex. As a control region, activity was isolated in the primary visual

cortex. Consistent with data showing that emotional processing is blunted in MJ Users

we hypothesized that MJ Users, compared to Controls, would judge fewer stimuli as

having emotional content and would show decreased functional activity and responses

magnitudes in brain areas involved in processing emotional content. Additionally, we

expect the largest differences to exist for stimuli judged to have negative content.

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METHODS

Participants

Seventeen chronic marijuana users (MJ Users) and 16 non-marijuana-smoking controls

(Controls) were included in the study. All participants were right-handed. Participants

were recruited through local media outlets or flyers posted throughout the community and

responded by contacting the laboratory over the phone. Following an initial phone

screen, participants were invited into the laboratory and agreed to participate in

procedures approved by the Wake Forest University School of Medicine Institutional

Review Board. On a first visit, participants provided urine samples to test for pregnancy

and drug use and were administered the Structured Clinical Interview for DSM-IV (First

1997) as well as the Wechsler Abbreviated Scale of Intelligence (Wechsler 1999).

Participants were excluded from the study if they presented with systemic diseases of the

central nervous system, head trauma, neurological disorders, Axis-I psychiatric disorders

(other than marijuana dependence for the MJ Users), abuse of substances other than

nicotine, or an I.Q. of less than 80. MJ Users were required to test negative for illicit

drugs other than marijuana and Controls were required to test negative for all illicit drugs.

Participants who met inclusion criteria were scheduled for a second visit (scan visit). MJ

Users were asked to abstain from using marijuana starting at midnight the night before

the scheduled scan visit.

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Procedure

On the day of the scan visit, participants received additional screening, cognitive testing

and task training prior to the acquisition of their fMRI scans. Once again, participants

provided urine samples to test for drug use and pregnancy as well as completed

depression (Beck’s Depression Inventory) and anxiety (Speilberger Test of Anxiety)

inventories. At no time did MJ Users report or overtly exhibit any signs of marijuana

withdrawal (Budney and Hughes 2006).

Approximately one hour before entering the scanner, participants were trained to perform

the emotional judgment task using a standard laptop computer and button box. This

allowed participants to become familiar with the layout and timing of the task. Prior to

performing the training session, participants visually followed along as task instructions

were read aloud by a study technician. Participants were informed that they would view

a series of photographs and then judge their emotional content. Participants were

informed that when a photograph appeared on the screen they were to initially process the

information and shortly thereafter they would be given the opportunity to make a

judgment regarding the photograph’s emotional content. After verbal acknowledgement

that they understood the instructions, participants completed a training run that consisting

of 10 stimuli. These stimuli were unique to the training run and were not repeated during

the scanner task. Participants were made aware of this distinction and performed an

additional run if the initial run resulted in mistimed events. Approximately, one hour

before the acquisition of their functional scans participants were given a 15 minute break

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with the opportunity to smoke a cigarette to avoid potential confounds of nicotine

withdrawal on functional brain activity (Wang et al. 2007; Xu et al. 2007). Two

participants in the MJ Users group and one in the Controls group took advantage of the

opportunity to smoke a cigarette.

Emotional Judgment (IAPS) Task

While in the scanner, participants viewed and judged 100 (80 emotional, 20 neutral)

normative stimuli (photographs) obtained from the International Affective Picture System

(IAPS) database (Lang et al. 2005). According standardized IAPS scores, stimuli

differed in valance (Positive, Neutral or Negative) and level of arousal (High or Low).

The stimuli included 20 positive high arousing, 20 positive low arousing, 20 neutral, 20

negative low arousing, and 20 negative high arousing photographs. Stimuli were

balanced so that each valence category contained an equivalent number of inanimate

objects and people (alone or in action scenes). This was done to minimize the chance of

observing differences due to emotional context and to ensure a generalized sampling of

emotional judgments.

The IAPS task was divided into two runs each lasting 8.75 minutes and starting after a 20

second countdown period. Each run contained 50 trials where participants viewed and

judged a total of 20 positive, 10 neutral, 20 negative photographs presented in

pseudorandom order. Participants first viewed and processed the content of each

stimulus then judged it as containing positive (POS), little to no (NEU), or negative

125

(NEG) emotional content by pressing 1, 2, or 3 on a button box positioned under their

right hand. On each trial a stimulus was shown for a total of 4.2 seconds. This was

followed by a jittered fixation period that ranged from 4.2 to 8.4 seconds. During the

first half of the 4.2 second viewing period (2.1 seconds), the processing component, the

stimulus appeared alone on the screen. During the second half of the 4.2 second viewing

period (2.1. seconds), the judgment component, a scale appeared below the stimulus and

participants recorded their emotional judgment. In the fixation period, participants stared

at a centrally located cross until the next viewing period began. On average, trials lasted

for 10.5 seconds.

Functional MRI data acquisition

Images were acquired on a 1.5T General Electric scanner with a birdcage-type standard

quadrature head coil and an advanced nuclear magnetic resonance echoplanar system.

The head was positioned along the canthomeatal line. Foam padding was used to limit

head motion. High-resolution T1-weighted anatomical images (3D SPGR, TR=10 ms,

TE=3 ms, voxel dimensions 1.0×1.0×1.5 mm, 256×256 voxels, 124 slices) were acquired

for coregistration and normalization of functional data. During each of the two runs a

total of 250 co-planar functional images were acquired using a gradient echoplanar

sequence (TR=2100 ms, TE=40 ms, voxel dimensions 3.75×3.75×5.0 mm, 64×64 voxels,

28 slices). Two radio frequency excitations were performed prior to image acquisition to

achieve steady-state transverse relaxation. The scanning planes were oriented parallel to

the anterior commissure–posterior commissure line and extended from the superior extent

126

of motor cortex to the base of the cerebellum. Six volumes of data were acquired during

the 20 second countdown period preceding each run and immediately discarded to allow

equilibrium before trials started.

Statistical Analyses

Behavior

The number of stimuli judged as having positive (POS), little or no (NEU), or negative

(NEG) emotional content was calculated for each individual. To determine if the two

groups differed in the number of stimuli judged to be POS, NEU, or NEG, a 2x3 analysis

of variance (ANOVA) was performed with between-subjects group factor (Controls and

MJ Users) and between-stimulus judgement type (POS, NEU and NEG).

Functional MRI preprocessing and data analysis

The functional data from each participant were corrected for acquisition time (slice

timing), realigned to the first volume (motion correction), normalized into a standardized

neuroanatomical space (Montreal Neurological Institute brain template), smoothed using

a Gaussian kernel of 8 mm, and high-pass filtered (128s) to remove low frequency noise.

Two analyses were performed in order to characterize functional brain activity while

viewing stimuli considered having emotional content. First, a whole brain analysis was

performed to examine functional differences throughout the entire brain associated with

viewing stimuli considered to have emotional content. For this analysis, a multiple

127

linear regression was performed. Regressors corresponded to the onset times of viewing

periods. Functional activity was isolated during the first half of the viewing period

(processing component; 2.1 seconds) for each stimulus type (POS, NEU, NEG),

according to each individual’s judgments recorded in the second half of the viewing

period. Activity for each stimulus type was convolved with a canonical hemodynamic

response function. A statistical contrast map was made for each individual by combining

the functional activity for trials in which stimuli were judged as having positive and

negative emotional content compared to the activity for trials in which stimuli were

judged as containing little or no emotional content (POS + NEG > NEU). These data

were modeled for all participants and compared within and between groups. Within

group comparisons were thresholded with a voxel-wise P value of 0.05 post-hoc

corrected using family-wise error correction. Between group comparisons were

thresholded with a voxel-wise P value of 0.005 (Gruber et al. 2009) further adjusted at

the cluster level (P <0.05, corrected). These analyses were performed using Statistical

Parametric Mapping 5 (SPM 5; Wellcome Department of Imaging Neuroscience,

London, UK) in the MATLAB 7.0 (Mathworks, Natick, MA) shell using an event-related

model (Friston et al. 1998). Reported voxels correspond to standardized MNI coordinate

space. Conversion to Talairach space was performed with the mni2tal script for

MATLAB to aid labeling of cortical brain areas with the Talairach Daemon software (see

http://www.talairach.org/).

In a second analysis, the magnitude (average percent signal change) of the functional

response for each stimulus type (POS, NEU and NEG) was isolated in anatomical regions

128

of interest (ROIs) known to be involved in emotional information processing and \ or

cognitive judgments. ROIs included bilateral 1) amygdala, 2) insula 3) medial prefrontal

cortex, including the anterior cingulate cortex (mPFC), 4) ventral medial prefrontal

cortex, including the orbital frontal cortex (vmPFC) and 5) rostral prefrontal cortex

(rPFC). ROIs were generated using WFU PickAtlas, version 2.4 (Maldjian et al. 2003).

ROIs not represented by name in WFU PickAtlas were generated using their

corresponding Brodmann Areas. The vmPFC contained BA 25 and 11, and the rPFC

corresponded to BA 10. As a control region, an ROI corresponding to the primary visual

cortex (BA17) was also generated. Next, for each individual the average BOLD signal

timecourse in each ROI was extracted for the entire task using MarsBaR (Brett et al.

2002). Each timecourse was interfaced with the corresponding individual’s SPM 5

regression model and the average percent signal change for events judged as POS, NEU

and NEG was calculated. To test for group differences in the magnitude of responses,

2x3 ANOVAs were performed for each ROI with between-subjects group factor

(Controls and MJ Users) and between-stimulus judgment type (POS, NEU and NEG).

Bonferroni post-hoc analyses were utilized to test for differences between groups within

each ROI.

129

RESULTS

Demographics

A description of study participants is shown in Table 1. Controls were 27.1 ± 6.3 years

old and did not meet dependence criteria for any illegal drug. Four of 10 members of the

control group reported previous marijuana use with use limited to fewer than 50 lifetime

uses, occurring more than 2 years prior to the study. MJ Users were 25.1 ± 3.1 years old

(mean ± sd) and reported using marijuana 4.3 ± 4.4 times a day, 29.3 ± 1.4 days a month,

for 10.2 ± 3.3 years. The average age of first marijuana use was 14.9 ± 2.0 years. On the

scanning day, no MJ Users or Controls tested positive for any illegal substances (other

than marijuana in MJ Users). All members of the MJ Users group tested positive for

marijuana metabolites on the day of scanning and reported a mean (± sd) abstinence from

marijuana of 13.0 ± 1.7 hours (range = 11 – 16 hours). Importantly, there were no

significant differences between groups in depression scores on the Becks Depression

Inventory, nor anxiety scores on the Spielberger Test of Anxiety before scanning.

130

Table 1. Group Demographics

Variable

Controls

(N = 16)

MJ Users

(N = 17)

Mean (± SD) Mean (± SD)t / X2

Valuep

Age (years) 27.1 (6.3) 25.1 (3.1) 1.17 n.s.

Full I.Q. 113.81 (7.4) 105.59 (15.1) 1.96 n.s.

Sex 1.59 n.s.

Male 5 9

Female 11 8

Cigarette Smokers 12.5 % 52.9% 6.07 0.03

Caffeine (mg/day) 103.1 (72.1) 108.7 (69.5) 0.98 n.s.

Alcohol AUDIT Score 3.3 (2.0) 4.8 (2.7) 1.76 n.s.

Spielberger State Anxiety 27.0 (10.0) 26.9 (6.3) 0.56 n.s.

Beck’s Depression 2.5 (3.2) 4.0 (2.9) 1.22 n.s.

Marijuana Use:

Age of onset (years) 14.9 (2.0)

Years of Total Use 10.2 (3.3)

Days per month 29.3 (1.4)

Times per day 4.3 (4.4)

Years at current use level 5.6 (3.3)

131

Behavior

Categorical judgments of each stimulus type are shown in Fig. 1. Controls and MJ Users

did not differ in the number of stimuli judged as having positive (POS), little or no

(NEU) or negative (NEG) emotional content F(1,24) = 0.276, p = 0.604. Controls

categorized a mean (± se) of 36.70 ± 1.9, 28.00 ± 2.1 and 34.00 ± 1.8 stimuli as being

POS, NEU and NEG, compared to MJ Users who categorized 35.47 ± 1.4, 29.80 ± 1.9,

33.73 ± 1.0, respectively.

05

10

15

20

25

30

35

40

Nu

mb

er o

f S

tim

uli

POS NEU NEG

Emotional Judgments

Controls

MJ Users

Figure 1. Distribution of the number of IAPS stimuli judged as

having positive emotional content (POS), little to no emotional

content (NEU), or negative emotional content (NEG) in Controls

and chronic marijuana users (MJ Users). There was no

difference between groups in the number of stimuli placed in

each category.

132

Whole Brain Activity and Emotional Stimuli

Whole brain analysis of functional activity while viewing stimuli judged to have

emotional content, relative to stimuli judged to have little or no emotional content (POS +

NEG > NEU) can be seen in Fig. 2. Controls and MJ Users had activity in similar brain

areas with a couple of notable exceptions. Both groups had greater activity in secondary

visual cortex (Controls: x y z = -8 -80 -2, KE = 846; MJ Users: x y z = -26 -76 -10, KE =

814) and part of the cerebellum (Controls: x y z = -18 -40 -50, KE = 92; MJ Users: x y z =

-2 -64 -52, KE = 96). Both groups also had greater activity in portions of the thalamus

(Fig. 2 Y= -28; Controls: x y z = 20 -28 -4, KE = 263; MJ Users: x y z = -16 -30 -4, KE =

613) and the cingulate cortex (Fig. 2 Y= 16; Controls: x y z = -8 -80 -2, KE = 846; MJ

Users: x y z = -26 -76 -10, KE = 814). Unlike MJ Users, however, Controls also had

greater activity in the right inferior frontal gyrus (Fig. 2 Y= 28; x y z = 36 28 -4, KE =

161) and the right amygdala (Fig. 2 Y= -4; x y z = 22 -4 -26, KE = 86) for stimuli judged

as having emotional content, compared to those judged as having little to no emotional

content.

Examining activity that was significantly less while viewing stimuli judged to have

emotional content, compared to stimuli judged to have little or no emotional content

(POS + NEG < NEU), revealed a significant functional cluster in MJ Users that was not

observed in Controls (Fig. 2, Y = 52). MJ Users had significantly less activity while

viewing stimuli considered to have emotional content in portions of the medial prefrontal

cortex (KE = 773). Of the three local maxima within this cluster, two were spatially

133

coincident with the anterior cingulate cortex (x y z = -10 52 -4; 2 52 0) while the other

was located in the medial prefrontal cortex (x y z = -16 60 14). There were no areas

where Controls had significantly less activity while viewing stimuli judged as having

emotional content, compared to stimuli judged to have little to no emotional content.

t scores

-10

-2

10

2

Emotional

Content

>

Little or no

Emotional

Content

Emotional

Content

<

Little or no

Emotional

Content

Y = - 28 - 4 16 28 52

Y = - 28 - 4 16 28 52

r

Controls

MJ Users

r

Figure 2. Brain activity in Controls and chronic marijuana users (MJ Users) in response to viewing

stimuli judged as emotional, compared to stimuli judged as having little to no emotional value.

Positive contrasts (hot) are clusters where activity was greater in response to emotional stimuli.

Negative contrasts (cold) are clusters where activity was less in response to emotional stimuli.

134

Direct comparisons between groups for stimuli judged as having emotional content (POS

+ NEG > NEU) is shown in Fig. 3. Compared to Controls, MJ Users had significantly

less activity in a functional cluster spatially coincident the medial frontal lobe (KE = 873).

Of the local maxima within the cluster, three were spatially coincident with the anterior

cingulate cortex (x y z = 4 36 0; 0 46 8; -6 32 26) and two with the medial prefrontal

cortex (x y z = 4 50 -6; -6 48 26). There were no areas where MJ Users had a greater

functional activity, compared to Controls, for stimuli judged as having emotional content.

Figure 3. Direct comparisons of brain activity between Controls and chronic marijuana users

(MJ Users) while viewing stimuli judged as having emotional content, compared to stimuli

judged as having little to no emotional content (NEU). Contrast map shows clusters where

viewing emotional stimuli (POS + NEG > NEU) was significantly less in MJ Users, compared

to Controls. There were no clusters where activity was significantly greater in MJ Users,

compared to Controls

135

Response Magnitudes in Emotional Networks

Analysis of the average percent change in BOLD signal from stimulus onset revealed

significant main effects of group in the left amygdala F(2,31) = .6.92, p = 0.013 and the

right insula F(2,31) = .6.25, p = 0.018 (Fig. 4). In both of these brain areas the response

magnitude for all stimuli, regardless of emotional judgment, was significantly greater in

Controls than MJ Users. In the left amygdala (Fig. 4a), the mean (± se) percent signal

change in Controls was 0.25 ± 0.03 while in MJ Users it was 0.09 ± 0.02. In the right

insula (Fig. 4b), the percent signal change in Controls was 0.09 ± 0.01, compared to 0.01

± 0.01 in MJ Users. Main effects of group were not observed in other ROIs, including

the primary visual cortex control region.

.05

.10

.15

.20

.25

.30

.02

.04

.06

.08

.10

.12

a. b.

All Stimuli All Stimuli

% S

ign

al

Ch

an

ge

Left Amygdala

Controls

MJ Users

Right Insula

**

Figure 4. Percent signal change in Controls and chronic marijuana users (MJ Users) for all stimuli

judged in the emotional task. Compared to Controls, MJ Users (MJ Users) had significantly smaller

response magnitudes in the left amygdala and right insula in response to all stimuli. * p < .05

136

Differences in response magnitudes according to judgment type can be seen in Fig. 5.

Group x valence interactions were observed in the amygdala F(2,62) = 4.81, p = 0.011,

the insula F(2,62) = 6.32, p = 0.003 and the medial prefrontal cortex F(2,62) = 3.60, p =

0.033. While activity in MJ Users was less for each of the stimulus types, after

correcting for multiple comparisons, significant differences were limited to stimuli

judged as having positive (POS) emotional content. In the left amygdala, the percent

signal change to POS in Controls was 0.27 ± 0.07, significantly greater than 0.05 ± 0.04

in MJ Users t(31) = 2.73, p = 0.01 (Fig. 5a). In the left insula, the response in Controls

was 0.09 ± 0.02, compared to 0.01 ± 0.02 in MJ Users t(31) = 3.18, p = 0.003 (Fig 5c).

In the right insula, the response was 0.10 ± 0.02 in Controls and -0.02 ± 0.02 MJ Users

t(31) = 4.08, p < 0.001 (Fig 5d). Interestingly, response magnitudes observed in the

medial prefrontal cortex appeared to be comparable in magnitude, but opposite in

direction. In the left medial prefrontal cortex, the response in Controls was 0.05 ± 0.02,

compared to -0.05 ± 0.03 in MJ Users t(31) = 2.80, p = 0.009 (Fig. 5e) while the response

in the right medial prefrontal cortex was 0.04 ± 0.02 in Controls and -0.06 ± 0.03 in MJ

Users t(31) = 2.82, p = 0.008 (Fig. 5f). Group x valence interactions were not observed

in the ventral medial prefrontal cortex, including the orbital frontal cortex, the rostral

prefrontal cortex, or the primary visual cortex control region.

137

Figure 5. Percent signal change in Controls and chronic marijuana users (MJ Users) for

each stimulus category on the emotional task, including positive emotional content

(POS), little to no emotional content (NEU), or negative emotional content (NEG).

Compared to Controls, MJ Users (MJ Users) had significantly smaller response

magnitudes while viewing stimuli judged as having positive emotional content in the

amygdala, insula and medial prefrontal cortex, including the anterior cingulate cortex

(mPFC). * p < .05

138

DISCUSSION

The results of the present study demonstrate that chronic marijuana users (MJ Users)

have altered brain activity while viewing emotional stimuli. These data are consistent

with studies demonstrating that functional responsiveness is decreased in MJ Users for

emotional stimuli presented below the level of consciousness (Gruber et al. 2009). The

current study extends these findings to include emotional stimuli that are consciously

judged to be emotional. There was no difference between groups in the stimuli

considered to be emotional. While viewing emotional stimuli, both groups had greater

activity in secondary visual cortex, posterior cerebellum, thalamus, and cingulate cortex.

Controls also had greater activity in the right middle frontal gyrus and the right amygdala

for emotional stimuli, which was not observed in MJ Users. In response to emotional

stimuli, MJ Users had hypoactive responses in the medial prefrontal cortex and anterior

cingulate cortex, and this activity was less than the emotional response evoked in

Controls. MJ Users had smaller response magnitudes in the left amygdala and right

insula for all stimuli and smaller response magnitudes in the left amygdala, bilateral

insula, the medial prefrontal cortex and anterior cingulate cortex for positive stimuli,

compared to Controls. These data suggest that while viewing stimuli considered as

having emotional value, MJ Users experience decreased functional responses in brain

areas involved in emotional/somatic information processing and/or cognitive judgments.

In present study, differences between groups were not observed in the number of stimuli

judged as having emotional content. While this runs counter to the original hypothesis,

that blunted functional responsivity in MJ Users would result in fewer stimuli considered

having emotional content, it is consistent with behavioral-functional discrepancies

139

observed in recreational marijuana users. In the Phan et al. (2008) study, participants

matched a target, consisting of emotional facial expressions (angry, fearful or happy) or a

shape, with one of two choices. It was found that as increasing doses of THC attenuated

the amygdala response to angry and fearful faces, no difference was observed in the

accuracy or response times for matching these stimuli (Phan et al. 2008). Data from the

current study, as well as the Phan et al. (2008) study, suggests a disconnect between

behavioral and functional responsiveness to emotional stimuli in MJ Users, but may also

reflect task measures that do not capture the behavioral consequences of the altered

functional processing of emotional stimuli. Unlike other studies, in the current study

visual stimuli were not limited to facial expressions known to elicit fear or threat

responses, and functional activity was not isolated in the context of a matching task

(Gruber et al. 2009; Phan et al. 2008) or in response to masked stimuli (Gruber et al.

2009; Phan et al. 2008). In the current study, participants were simply asked to process

and judge the emotional content of visual stimuli. This was done in order to sample more

general, conscious emotional processes not confined by social context. To this end, the

functional activity reported in the current study may reflect more objective processing

related to emotional content, as opposed to subjective responsivity. The behavioral data

in the current study suggest that while experiencing decreased brain activity during the

processing of general emotional stimuli, MJ Users retain the ability to objectively

identify emotional content of these stimuli.

While viewing stimuli judged as having emotional content, compared to stimuli judged as

having little to no emotional content, MJ Users lacked activity in the right middle frontal

140

cortex and right amygdala that was observed in Controls. This is consistent with data

from animal studies showing that cannabinoids in the amygdala modulate functional

responses to affective stimuli in animals (Lin et al. 2006; Marsicano et al. 2002) and

humans (Phan et al. 2008). MJ Users also had decreased activity in the medial prefrontal

cortex and anterior cingulate for stimuli judged as having emotional content, and this

activity was significantly less than Controls. As these areas have been shown to be the

interface for emotional processing related to executive functioning (Bechara et al. 2000;

Bechara et al. 2001; Chambers et al. 2006; Johnson et al. 2008), this suggests potential

abnormalities in higher order processing of emotional in MJ Users. This is consistent

with data that shows MJ Users lack an affective response in this brain area during

complex decision-making that predicts learning in Controls (Wesley et al. in press). The

data from the current study extends these findings to include abnormal functioning in this

area outside the constraints of complex decision-making for stimuli consciously judged to

have emotional content.

Analysis of percent signal change for all stimulus categories revealed that, compared to

Controls, MJ Users had decreased responses in the left amygdala and right insula for all

stimuli judged in the task. This suggests that MJ Users may have a deficit in these areas

while attempting to identify the emotional content of a stimulus, regardless of its

emotional valence. Interestingly, compared to Controls, MJ Users had decreased

response magnitudes in the left amygdala, bilateral insula, and bilateral medial prefrontal

cortex that was specific to stimuli judged as having positive emotional content. This is

unique from what has been observed in animals, where most studies focus on the ability

141

of cannabinoids to alter aversive or negative information processing in the amygdala (Lin

et al. 2006; Marsicano et al. 2002). This finding is different from what was shown in the

Phan et al. (2008) study, where a THC-mediated increases were observed in amygdala

response to positive stimuli (happy faces) in recreational marijuana users, a finding

consistent with the prosocial effects of THC (Foltin and Fischman 1988) and its enhanced

processing of reward signals (Gardner 2005). However, the current findings are

consistent with the Gruber et al. (2009) study, where decreased amygdala and anterior

cingulate activity were observed in response to positive stimuli (happy faces) presented

below the level of consciousness. In that study and the current study, participants were

long-term heavy marijuana users (MJ Users) with similar demographic characteristics.

Therefore, decreased functional responses to positive emotional stimuli may be unique to

long-term heavy marijuana users. In the Gruber et al. (2009) study, decreases were also

observed in response to negative emotional stimuli, which was not the case in this study.

Differences in study design may help explain this discrepancy in findings. The Gruber et

al. (2009) study examined functional responses to emotional stimuli presented below the

level of consciousness whereas the current study examined conscious processing of

emotional stimuli. The Gruber et al. (2009) study presented emotional facial expressions

whereas the current study consisted of a general sample of emotional stimuli. Finally,

participants in the current study were asked to make a judgment regarding the emotional

content of a stimulus, which may reflect more cognitive/emotional processing as opposed

to subjective emotional responses. This may explain the consistent decreases observed in

the medial prefrontal cortex and anterior cingulate cortex of MJ Users. The data from the

142

current study suggests that MJ Users have decreased functional activity to stimuli judged

as having positive content.

There are limitations of the current study based on the task design and analysis that may

warrant further investigation. There were more cigarette smokers in the MJ Users group

than the Controls group. Analysis of the smokers (n = 9) and non-smokers (n = 8) in the

MJ User group however revealed no difference in behavioral performance or brain

activity. Furthermore, the number of cigarettes smoked per day was incorporated as a

covariate in the imaging analyses, similar to other imaging studies with significant

differences in demographic variables between groups (Bolla et al. 2005). The current

study used emotional stimuli obtained from a standardized data base of visual images. It

is also unclear if similar results would be observed for more personalized emotional

stimuli. For example, one study has demonstrated that recall of autobiographical happy

events result in increased functional activity in several brain areas, including medial

prefrontal areas (Cerqueira et al. 2008). Altered functional activity in MJ Users for

personalized emotional information may have greater clinical relevance, especially in the

increasing population of treatment seekers reporting marijuana as there number one

problem drug (Compton et al. 2004). Given the established relationship between

cannabinoid function and the expression psychotic symptoms (D'Souza 2007), it is of

clinical interest to know how emotional processing differs in MJ Users who do and do

not express psychotic symptoms. Finally, the current study does not rule out influences

of preexisting conditions including psychiatric disorders or genetic background.

143

To summarize, in the current study there was no difference between Controls and chronic

long-term marijuana users (MJ Users) in stimuli judged as containing positive, little to

no, or negative emotional content. Despite the ability to correctly identify the emotional

content of these stimuli, MJ Users had altered brain activity while processing this

information. For stimuli judged as having emotional content, compared to those judged

as having little to no emotional content, MJ Users lacked activity in the right middle

frontal cortex and right amygdala that was observed in Controls. Furthermore, MJ Users

had large decreases in activity in the medial prefrontal cortex and anterior cingulate to

emotional stimuli. Direct comparisons between groups revealed that activity in the

medial prefrontal cortex and anterior cingulate cortex of MJ Users was significantly less

than that of Controls for emotional stimuli. MJ Users also had decreased response

magnitudes in the left amygdala and right insula, compared to Controls, while processing

all emotional stimuli. Finally, MJ Users had decreased response magnitudes for stimuli

judged as having positive content in the left amygdala, bilateral insula and bilateral

medial prefrontal cortex, including the anterior cingulate cortex, compared to Controls.

Together, these data demonstrate that MJ Users have decreased functional activity for

stimuli consciously judged to be emotional. These data suggest a discrepancy may exist

in MJ Users between the ability to correctly identify emotional content versus correctly

process this information. Future studies should focus on the clinical consequences of

decreased functional processing of stimuli judged to be emotional in MJ Users.

144

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CHAPTER V

SUMMARY AND CONCLUSIONS

Cannabis is a plant that for centuries has been used for various agricultural,

industrial, medicinal, and recreational purposes. The flowers of the cannabis plant,

marijuana, contain cannabinoid compounds that, as reviewed in the introduction of this

manuscript, have the ability to alter brain function and produce a multitude of effects.

Despite federal penalties and enforcement provisions against marijuana, it is the most

widely used illicit drug in the United States and both medicinal and recreational use is

increasing. This is a cause for concern, as many of the “impairments” observed in long-

term heavy marijuana users (MJ Users), such as altered learning, memory, attention, and

executive function (Pope et al. 2001; Solowij et al. 1991; Solowij et al. 2002), remain

poorly understood. The present series of studies was designed to further examine deficits

associated with long-term marijuana focusing on altered brain function during decision-

making and affective or emotional processing. The data from these studies demonstrate

that MJ Users have functional insensitivities of affective information during complex

decision-making and while making emotional judgments. These diminished functional

responses occur in several brain areas, including medial prefrontal brain regions that

subserve executive functioning abilities.

Previous studies had revealed poor behavioral performance in MJ Users while

performing the Iowa Gambling Task (IGT), a complex decision-making task considered

to have “real world” relevance (Hermann et al. 2009; Whitlow et al. 2004). As decision-

making is a multifaceted process requiring independent and integrative neural systems

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(Li et al. 2010), the underlying source of this deficit in MJ Users was not clear. A major

aim of the present series of studies, therefore, was to use the IGT to isolate brain activity

during specific components of the decision-making process and examine how it differed

between Controls and MJ Users. First, activity across the entire IGT was isolated during

selection and evaluation components. During these components, participants either

implemented behavioral choices or received feedback about the consequences of their

choices. Next, focus was placed the on role evaluation plays during strategy

development. For these analyses, activity in response to evaluation was isolated during

the early phase of the IGT, before behavioral differences in performance emerged

between groups. A final aim focused on brain activity in response to MJ Users and

Controls making simple emotional judgments. For this aim, activity was isolated as MJ

Users and Controls viewed stimuli from the International Affective Picture System

(IAPS) and judged their emotional content. The initial analysis of brain activity during

the various components decision-making yielded several important findings:

Increased function while implementing choices. During the selection

component of the IGT, MJ Users exhibited greater functional responses than Controls.

Selection elicited activity in the precuneus, postcentral gyrus, insula and middle cingulate

cortex of MJ Users that was not observed in Controls. Compared to Controls, MJ Users

demonstrated significantly greater activity in the insula and parahippocampal gyrus.

Insula activity is known to increase during anxious experiences (Craig 2009), especially

in anxiety prone individuals (Stein et al. 2007), and greater responses in the

parahippocampal gyrus has been associated with decreased memory function (Nestor et

al. 2008). In MJ Users, disadvantageous selections evoked significantly greater

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responses in the posterior cingulate cortex, insula and somatosensory association cortex

than Controls. Posterior cingulate cortex activity has been shown to increase in response

to emotional experiences (Maddock et al. 2003) as well as attentional demand (Small et

al. 2003). Since disadvantageous decks produce the largest immediate monetary gains,

MJ Users may focus more on selecting from these decks. Together, these results suggest

that as MJ Users implement behavioral choices during complex decision-making, they

have increased affect and attention, but diminished memory function, relative to Controls.

Decreased function to the consequences of choices. The evaluation component

of the IGT requires participants to view the positive and negative consequences of their

choices (i.e. monetary wins or losses) and integrate this information into on-going

decision-making processes. In MJ Users, the evaluation component evoked less activity

than in Controls. This finding was opposite to the observations made during the selection

component of the task. In Controls, evaluation elicited activity in the left dorsal lateral

prefrontal cortex (DLPFC) and the middle and anterior cingulate cortex that was not

observed in MJ Users. The evaluation of negative feedback (i.e. monetary losses) evoked

significantly less activity in the superior parietal cortex, posterior cingulate cortex and

somatosensory association cortex of MJ Users, compared to Controls. These data

suggests that MJ Users have diminished emotion- and attention-related functional

responses while receiving negative feedback about behavioral performance. As monetary

losses represent the necessary information needed to solve the IGT, functional

insensitivity to this feedback may explain poor decision-making in MJ Users. This is

supported by the fact that significant differences between groups were not observed in the

functional response to winning money.

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Together, analysis of the specific components of decision-making revealed that MJ Users

have significantly greater activity in emotion- and attention-related brain areas as they

implement behavioral choices but significantly less activity in these areas while

evaluating the negative consequences of their choices. As the evaluation of behavioral

performance guides future decisions, functional insensitivity to negative information may

explain the poor decision-making observed in MJ Users.

Decreased response to negative feedback during strategy development. In order

to examine the role of evaluation in the development of problem solving strategies, the

next analysis focused the functional response to evaluation during the earliest stage of the

IGT. During this early phase, groups did not differ in behavioral performance, as

successful performance had not yet emerged in Controls. Consistent with our hypothesis,

MJ Users were functionally insensitive feedback during strategy development. Feedback

evoked less activity in the anterior cingulate cortex, the ventral medial prefrontal cortex

and portions of the superior medial frontal cortex of MJ Users, compared to Controls.

MJ Users were specifically insensitive to the evaluation of monetary losses in these brain

areas. This further highlighted that poor decision-making in MJ Users was associated

with a functional insensitivity to aversive consequences. This point was confirmed when

the functional response to losses was correlated with the degree of learning achieved on

the IGT. Unlike Controls, MJ Users did not exhibit a significant relationship between the

functional response to losses and the degree of learning achieved on the task.

Specifically, in Controls larger functional responses in the medial prefrontal cortex, the

anterior cingulate cortex, and rostral prefrontal cortex to monetary losses predicted better

task performance in the future. This is consistent with data demonstrating that activity in

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the medial prefrontal cortex and anterior cingulate is necessary for successful

performance on the IGT (Bechara et al. 1994). Interestingly, the functional response

winning money did not correlate with task performance in either group, suggesting that it

is the degree of functional response to losses that drives successful decision-making

performance.

Data from the first two aims of these studies demonstrate that MJ Users have

functional deficits throughout the decision-making process, compared to Controls. As

MJ Users examine selection options and execute behavioral choices, they experience

increased function in emotion- and attention-related brain areas. MJ Users are

functionally insensitive, however, to feedback regarding the negative consequences of

their choices. The functional insensitivity to aversive consequences appears to be the

major factor contributing to their inability to develop successful problem solving

strategies. Since MJ Users do not “experience” the negative consequences of their

choices, they are not motivated to change or update on-going problem solving strategies.

MJ Users do not appear to be insensitive to the positive consequences of their choices,

however, which may explain why they continue to engage strategies that produce the

largest immediate rewards (i.e. selecting disadvantageous decks).

Altered activity to stimuli judged to have emotional content. The final aim of the

present series of studies focused the conscious processing of emotional stimuli. Several

lines of research suggested that MJ Users may exhibit diminished responses for stimuli

considered to be emotional. It was previously shown that Δ9-THC dose-dependently

decreases the functional response to signals of threat in recreational users (Phan et al.

2008). In MJ Users, similar to those in the present studies, diminished responses to

155

positive and negative stimuli were reported for stimuli presented below the level of

consciousness (Gruber et al. 2009). Our own data demonstrate that MJ Users have

diminished responses to negative feedback in the context of on-going decision-making

(Wesley et al. 2010). To examine the response to emotional stimuli, the brain activity

was isolated as individuals viewed IAPS stimuli judged as emotional. Groups did not

differ in the stimuli judged as emotional. However, consistent with our hypotheses, MJ

Users had diminished brain activity in response to emotional stimuli. Emotional stimuli

did not evoke activity in the right middle frontal gyrus and right amygdala of MJ Users,

which was observed in Controls. MJ Users also displayed significant hypoactive

responses in the medial prefrontal cortex and anterior cingulate cortex to emotional

stimuli. This hypoactive response was significantly less than the activity evoked by

emotional stimuli in Controls. Interestingly, hypoactive responses in this area have been

associated with attention in the absence of affective content (Simpson et al. 2001),

suggesting MJ Users do not exhibit an emotional functional response to stimuli judged as

emotional . MJ Users also displayed significantly smaller response magnitudes to

positive stimuli in the medial prefrontal cortex, anterior cingulate cortex, amygdala and

insula, compared to Controls. These data suggest that although MJ Users correctly

identify the emotional content of stimuli, they do not functionally respond to these stimuli

as emotional.

The goal of the present series of studies was to understand why MJ Users perform

poorly during complex decision-making and to explore affective processing in MJ Users.

We found that poor decision-making was due to functional insensitivities in MJ Users to

the negative feedback that typically guides behavioral performance. We also found that

156

as MJ Users identify emotional content, it evokes less of a functional response in

emotion-related brain areas. These data show that the functional response to affective

information is blunted during complex decision-making and while making simple

emotional judgments in MJ Users. They also suggest that the MJ Users have a

diminished “experience” of affective information.

The medial prefrontal cortex and anterior cingulate cortex. A consistent

observation from the present series of studies is that the functional response to affective

stimuli is diminished in the medial prefrontal cortex and the anterior cingulate cortex of

MJ Users. Responses in these brain areas were decreased in the context of complex

decision-making as well as making simple emotional judgments. This is highlighted in

Figure 1. Figure 1a shows the difference in brain activity in response to negative

feedback during the strategy development phase of the IGT. As shown, monetary losses

during strategy development evoked significantly less activity in the medial prefrontal

cortex and anterior cingulate cortex of MJ Users, as compared to Controls. Figure 1b

shows the relationship between the functional response to monetary losses during

strategy development and the degree of successful performance achieved on the IGT. In

Controls, the response to monetary losses during strategy development positively

correlated with the degree of successful performance achieved on the task. Specifically,

the larger the functional response in the medial prefrontal cortex and the anterior

cingulate cortex to early monetary losses the better Controls performed by the end of the

task. This relationship was not observed in MJ Users, suggesting that the insensitivity to

early monetary losses prevented the development of successful decision-making

performance on the IGT. While making simple emotional judgments on the IAPS task

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MJ Users also demonstrated diminished functional responses in the medial prefrontal

cortex and anterior cingulate cortex. Figure 1c shows a hypoactive response in MJ Users

while viewing emotional stimuli, compared to neutral stimuli. Figure 1d demonstrates

that this hypoactive response was significantly less in MJ Users, compared to Controls.

Together these demonstrate that regardless of the context of affective information,

complex decision-making (IGT) or making simple emotional judgments (IAPS), MJ

Users are functionally insensitive to this information in the medial prefrontal cortex and

anterior cingulate cortex. Decreased responsiveness explains the decision-making

deficits previously reported in MJ Users (Bolla et al. 2005; Hermann et al. 2009; Whitlow

et al. 2004). Damage to these areas has been shown to result in insensitivities to future

consequences (Bechara et al. 1994) and these areas are known to be involved in making

affective judgments (Northoff et al. 2006). Decreased responsiveness in the medial

prefrontal cortex and anterior cingulate cortex has important implications for all

executive function abilities that require affective information processing.

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Executive function. Executive function is used to define an overarching or

higher-order system in the brain that that coordinates processes responsible for abstract

thinking, internal / external sensory gating, planning, rule learning, initiating appropriate

actions and inhibiting inappropriate actions and cognitive flexibility (Struss and Knight

2002). This system is highly developed and is the basis by which humans reify

Figure 1. Functional activity evoked by emotional stimuli in the medial prefrontal cortex

and anterior cingulate cortex a) A significantly smaller response in MJ Users, compared

to Controls, to monetary losses during strategy development on the Iowa Gambling Task

(IGT) b) The functional response to monetary losses during strategy development on the

IGT predicts learning in Controls but not MJ Users c) A significantly smaller response in

MJ Users for emotional judgments, compared to neutral judgments, on the International

Affective Picture System (IAPS) task d) A significantly smaller response in MJ Users for

emotional judgments, compared to Controls, on the IAPS task

Loss Activity:

MJ Users < Controls

IGT

IAPS

a

c d

Loss Activity that

Predicts Learning

Emotional Activity:

MJ Users

Emotional Activity:

MJ Users < Controls

b

Controls

MJ Users

159

constructs for understanding and communication. The ability to process and integrate

complex information in real-time in a functionally relevant way suggests that executive

functioning plays an integral role in an individual’s comprehension of, and involvement

in, reality. The medial prefrontal cortex and anterior cingulate cortex are the interface

between cognitive and affective processing streams in the brain and are crucial for

normal executive function (Bechara et al. 2001; Dolcos et al. 2005; Gusnard et al. 2001;

Johnson et al. 2008; Simpson et al. 2001; Small et al. 2003). The data from the present

series of studies demonstrate that due to functional insensitivities to affective information

in these areas, MJ Users have diminished executive function abilities.

There are several hypotheses that address how decreased responsiveness in the medial

prefrontal cortex and anterior cingulate cortex may contribute to compromised executive

function in MJ Users. It has been hypothesized that decreased activity in these areas

results in poor error monitoring (Lin et al. 2008) and general performance monitoring

related to expectancy deviation (Oliveira et al. 2007). It has also been hypothesized that

reductions reflect decreased motivation (Martin-Soelch et al. 2009; Simoes-Franklin et al.

2009). In the present series of studies, however, differences in performance monitoring

and / or motivation do not appear to be significant contributing factors. All individuals

completed the tasks, and groups did not differ in the number of omitted or “no response”

events, suggesting that all individuals were motivated to perform the tasks. On IGT

selection trials immediately following monetary losses, both Controls and MJ Users

shifted selections away from loss producing decks more than 95% of the time. This

suggests that both groups successfully recognized “error” events and this motivated

changes in selection strategies in both groups.

160

It has also been hypothesized that function in the medial prefrontal cortex and anterior

cingulate cortex represents a balance between cognitive and affective processing during

executive function (Bechara et al. 1994; Bechara et al. 2005). This is supported by data

showing that function is increased in response to affective information and decreased by

attention-demanding information that lacks affective content (Gusnard et al. 2001). As

such, this balance informs executive functioning and shapes our experiences and future

behaviors. For example, decreased integration of affective information into executive

function has been used to explain insensitivities to future consequences in patients with

lesions to these brain areas (Bechara et al. 1994). Our data fit this model, and suggest

functional insensitivities in MJ Users shifts this balance away from functional affective

responses toward more functional cognitive responses. In other words, MJ Users

functionally treat affective information as more cognitive information that is devoid of

emotional content. This is supported by our observation that MJ Users correctly

recognize affective information, but do not display emotional functional responses to this

information. Thus, affective cues are less likely to guide executive functioning processes

in MJ Users. This can result in differences in the way Controls and MJ Users experience

and comprehend reality.

Limitations. One consistent limitation in the studies in this dissertation involves

differences in nicotine usage between Controls and MJ Users. In each study, a

significantly larger proportion of the MJ Users were also cigarette smokers. While this

variable was treated as a nuisance variable in all of the imaging analyses, it is possible

that subtle differences may exist between these groups due to the effects of nicotine. In

an attempt to help rule out this possibility, additional analyses revealed no behavioral and

161

/ or functional differences between the MJ Users who were and were not cigarette

smokers.

Future studies. Future studies are needed to help determine the extent to which

affective and cognitive function is altered in MJ Users. The interpretation of data from

the current studies should be considered in the terms the design parameters employed.

From the IGT studies, which require relatively fast information processing, it is not clear

how altering temporal dynamics may influence affective and cognitive integration and

change behavioral performance. More research is needed to determine if decision-

making abilities would improve in MJ Users if they were given more time to process

affective information. This could be determined by extending the IGT evaluation times

and measuring behavioral performance. If performance improves in MJ Users with

extended evaluation times, then this would suggest that it simply takes longer for MJ

Users to integrate affective cues into executive functioning in a behaviorally relevant

way. If performance does not improve with extended time, then that would suggest MJ

Users have a more global deficit where affective stimuli have lost behavioral relevance.

It is also unclear if decision-making would improve as a function of motivation. This

could be determined by manipulating the reward salience associated with IGT

performance. For example, based on task performance MJ Users could have the option

of receiving various amounts of real money or marijuana. If performance improved

under conditions where rewards were greater, then this would suggest that integration of

affective and cognitive information may be restored by increasing motivation. If

performance did not improve, then this would suggest that successful functional

integration may require more than just “wanting it”. Finally, in order to determine if

162

these functional alterations are reversible, these studies should be performed in MJ Users

abstaining from marijuana use for various lengths of time.

It is unclear if altered functional processing exists for stimuli considered to have personal

emotional value. This could be determined by recording brain activity in MJ Users as

they listen to personalized emotional scripts and are asked to recall this information. If

more personalized stimuli recover normal affective responses then this would suggest

that decreased emotional functioning is limited to stimuli without personal value. If

function remains blunted for personalized stimuli, then this suggests a general functional

deficit to emotional stimuli. Similar to the suggested studies for decision-making,

abstinence studies should be performed to understand if emotional processing can be

restored by the cessation of marijuana use.

Final considerations. The data from the present series of studies are important

for several reasons. They are evidence of a negative consequence associated with long-

term heavy marijuana use. These data should be considered by healthcare professionals

as well as medicinal and recreational marijuana users. In terms of medicinal use,

marijuana is often used to alleviate or decrease stress and anxiety. While this may be an

initial benefit of marijuana use, chronic use may produce functional insensitivities to

affective information. Users should be aware that the functional insensitivities associated

with chronic use can impair decision-making and alter the experience of emotions.

Particular caution should be taken by users who have preexisting conditions associated

with disrupted affective and / or executive processes that may be exacerbated by long-

term marijuana use. Similarly, caution should be taken when using marijuana to treat

symptoms associated with stress and psychological disorders. For example, in the case of

163

post traumatic stress disorder (PTSD), though acute marijuana may relieve symptoms of

hyperarousal, prolonged use may impair normal arousal by inducing additional

hypoactivity in the medial prefrontal cortex (Koenigs and Grafman 2009).

The results from these studies should also be considered by clinicians attempting

to treat individuals with marijuana abuse disorders. In recent years the number of

individuals entering treatment facilities reporting marijuana as their major problem drug

has increased (Compton et al. 2004). As part of a treatment strategy, clinicians should

consider that MJ Users may have compromised integration of cognitive and emotional

information. As suggested by the IAPS study, decreased functional responses to affective

stimuli can exist even when these stimuli are considered as emotional. Cognitive based

behavioral treatments may benefit from reestablishing “normal” functional integration of

emotional information into cognitive processes. Such an approach might be aided by

pharmacotherapy techniques aimed at reestablishing normal functional responsiveness to

emotional stimuli. Theoretically, this would shift the boundaries of executive function

and establish new realities where treatment of marijuana abuse may be more efficacious.

Finally, these results should be considered by those who recreationally abuse

marijuana for long periods of time. The data suggest these individuals may alter brain

processes beyond the experience of the psychoactive “high” that maintains their abuse.

In the present series of studies, decreased functional responsiveness to affective

information was associated with poor problem solving abilities and MJ Users had

decreased responsiveness to stimuli judged as emotional. These consequences of long-

term use are likely to have negative professional and personal consequences. Ironically,

due to blunted affective processing MJ Users may not interpret these consequences as

164

negative. Nonetheless, they may perform poorly in situations that require affective and

cognitive integration. MJ Users may be less likely to engage in successful trouble

shooting and problem solving, compared to those who do not abuse marijuana. To this

end, MJ Users may be compromising skills necessary to navigate successfully in

competitive work environments and / or interpersonal relationships.

165

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users is associated with decreased functional responsiveness to negative

consequences. Psychiatry Research: Neuroimaging

Whitlow CT, Liguori A, Livengood LB, Hart SL, Mussat-Whitlow BJ, Lamborn CM,

Laurienti PJ, Porrino LJ (2004) Long-term heavy marijuana users make costly

decisions on a gambling task. Drug Alcohol Depend 76: 107-11

169

CURRICULUM VITEA

Michael J. Wesley

__________________________________________________ November 8th

, 2010

DEMOGRAPHIC AND PERSONAL INFORMATION

Current Appointment

Doctoral Candidate

Department of Physiology & Pharmacology

Wake Forest University School of Medicine

Personal Data

Medical Center Blvd.

Winston-Salem, NC 27110

(336) 716-8564

(336) 716-8689 Fax

mwesley@wfubmc.edu

EDUCATION

1997-1999 Associate of Arts Bainbridge College Psychology

1999-2001 Bachelor of Arts Berry College Psychology

2003-2004 Graduate Student Georgia State University Biology

2004-present Graduate Student Wake Forest University Physiology and

Pharmacology

PROFESSIONAL EXPERIENCE AND TRAINING

1999-2001 Student Researcher Berry College Dept. of Psychology

2000-2001 Research Assistant Emory University Division of Psychobiology

2001-2003 Research Specialist Emory University Depts. of Pharmacology,

Psychiatry

170

RESEARCH ACTIVITIES

Manuscripts

1. Hopkins WD, Fernandez-Carriba S, Wesley MJ, Hostetter A, Pilcher D and Poss S

(2001) The use of bouts and frequencies in the evaluation of hand preferences for a

coordinated bimanual task in chimpanzees (Pan troglodytes): An empirical study

comparing two different indices of laterality. J Comp Psychol 115: 294-99

2. Hopkins WD, Cantalupo C, Wesley MJ, Hostetter AB and Pilcher DL (2002) Grip

morphology and hand use in chimpanzees (Pan troglodytes): Evidence of a left

hemisphere specialization in motor skill. J Exp Psychol Gen 131(3): 412-23

3. Hopkins WD and Wesley MJ (2002) Gestural communication in chimpanzees (Pan

troglodytes): The influence of experimenter position on gesture type and hand preference.

Laterality 7(1): 19-30

4. Wesley MJ, Fernandez-Carriba S, Hostetter A, Pilcher D, Poss S and Hopkins WD

(2002) Factor analysis of multiple measures of hand use in captive chimpanzees: An

alternative approach to the assessment of handedness in nonhuman primates. Int J Prim

23: 1155-68

5. Hopkins WD, Stoinski TS, Lukas KE, Ross SR and Wesley MJ (2003) Comparative

assessment of handedness for a coordinated bimanual task in chimpanzees (Pan

troglodytes), gorillas (Gorilla gorilla) and orangutans (Pongo pygmaeus). J Comp

Psychol 117(3): 302-8

6. Leavens DA, Hostetter AB, Wesley MJ and Hopkins WD (2004) Tactical use of

unimodal and bimodal communication by chimpanzees, (Pan troglodytes). Animal

Behaviour 67: 467-76

7. Hopkins WD, Wesley MJ, Izard MK, Hook M and Schapiro SJ (2004) Chimpanzees

(Pan troglodytes) are predominantly right-handed: Replication in three populations of

apes. Behav Neurosci 118(3): 659-63

8. Tang W, Wesley MJ, Freeman WM, Liang B and Hemby SE (2004) Alterations in

ionotropic glutamate receptor subunits during binge cocaine self-administration and

withdrawal in rats. J Neurochem 89:1021-33

9. Hopkins WD, Wesley MJ, Russell JL, Schapiro SJ (2006) Parental and perinatal factors

influencing the development of handedness in captive chimpanzees. Dev Psychobiol

48(6): 428-35

171

10. Hanlon CA, Wesley MJ, Porrino LJ (2009) Decreased functional specificity in the dorsal

striatum of chronic cocaine users, Drug and Alcohol Depend Jun 1(102): 88-94

11. Hanlon CA, Wesley MJ, Miller MD, Porrino LJ (2010) Loss of laterality in chronic

cocaine users: an fMRI investigation of sensorimotor control, Psychiatry Research

181(1): 15-23

12. Hanlon CA, Wesley MJ, Stapleton JR, Laurienti PJ, Porrino LJ. The association between

frontal-striatal connectivity and sensorimotor control in cocaine users. Drug and Alcohol

Dependence, in press

13. Hanlon CA, Dufault DA, Wesley MJ, Porrino LJ, Elevated gray and white matter density

in cocaine users relative to cocaine abstainers, Biological Psychiatry, in revision

14. Wesley MJ, Hanlon CA, Porrino LJ. Poor decision-making by chronic marijuana users is

associated with decreased functional responsiveness to negative consequences.,

Psychopharmacology, Psychiatry Research: Neuroimaging, in press

15. Wesley MJ, Hanlon CA, Porrino LJ. Neurofunctional correlates of poor decision-making

in chronic marijuana users, in preparation

16. Wesley MJ, Hanlon CA, Porrino LJ. Decreased functional responsiveness in chronic

marijuana users to stimuli considered to be emotional, in preparation

Peer Reviewed Abstracts

1. Wesley MJ & Hopkins WD. Factor analysis of multiple measures of hand use in captive

chimpanzees (Pan troglodytes): An alternative approach to the assessment of hand use in

nonhuman primates. Atlanta, GA: Emory University SURE program, 2001.

2. Wesley MJ, Stanley L & Hemby SE. Extracellular Levels of GABA in Ventral pallidal

sub-regions of physically dependent rats during response-dependent and response-

independent morphine administration. Atlanta, GA: Emory University SURE program,

2002.

3. Wesley MJ & Hemby S. Cocaine self-administration and ionotropic glutamate receptors:

Of rats and men. San Diego, CA: National Institute on Drug Abuse, Satellite Convention,

2004.

4. Wesley MJ, Horman

B, Morales

J, Parsons

LH, & Hemby

SE. Comparison of

extracellular amino acid concentrations in ventral pallidal subregions during contingent

and non-contingent morphine administration in opiate-dependent rats. Program No. 119.3

2004 Abstract Viewer/Itinerary Planner. San Diego, CA: Society for Neuroscience, 2004.

Online.

172

5. Freeman WM, Wesley MJ, Vrana KE, Pruess TM, Hopkins WD. Proteomic

characterization of the non-human primate brain. Munich, Germany: Human Proteome

Organization, 2005.

6. Wesley MJ, Hanlon CA, Miller MD, Livengood LB, Laurienti PJ, Porrino LJ.

Neurofunctional correlates of decision-making success in marijuana users. Program No.

123.4 2006 Abstract Viewer/Itinerary Planner. Atlanta, GA: Society for Neuroscience,

2006. Online. (oral presentation)

7. Wesley MJ, Hanlon CA, Livengood LB, Kraft R, Zhu JM, Wyatt C, Porrino LJ. Chronic

marijuana users show decreased integrity in neural pathways associated with executive

function and memory. College of Problems on Drug Dependence annual meeting, 2006.

Online.

8. Hanlon CA, Wesley MJ, Livengood LB, Roth AJ, Hampson RE, Porrino LJ, Decreased

functional specificity in the dorsal striatum of chronic cocaine users: an fMRI

investigation. Program No. 912.17 2007 Neuroscience Meeting Planner. San Diego, CA:

Society for Neuroscience, 2007. Online.

9. Wesley MJ, Hanlon CA, Miller MD, Livengood LB, Laurienti PJ, Porrino LJ. Chronic

marijuana users show altered behavioral and neural response to monetary loss. Program

No. 740.20 2007 Neuroscience Meeting Planner. San Diego, CA: Society for

Neuroscience, 2007. Online.

10. Wesley MJ, Hanlon CA, Miller MD, Livengood LB, Laurienti PJ, Porrino LJ. Chronic

marijuana users show altered neural processing during decision making and feedback.

International Cannabinoid Research Society annual meeting, 2007. Online. (oral

presentation)

11. Hanlon CA, Wesley MJ, Roth AJ, Porrino LJ. Sensorimotor Integration deficits in

chronic cocaine users: an fMRI and DTI study. College of Problems on Drug

Dependence Annual Meeting, June 2008. Online.

12. Dufault DL, Hanlon CA, Wesley MJ, Porrino LJ. Cocaine abstainers have greater white

matter densities than current cocaine users. College of Problems on Drug Dependence

Annual Meeting, June 2008. Online.

13. Hanlon CA, Wesley MJ, Roth AJ, Porrino LJ. Loss of neurofunctional laterality in the

frontal cortex of chronic cocaine users: a fMRI and DTI study. Program No. 57.23 2008

Neuroscience Meeting Planner. Washington D.C.: Society for Neuroscience, 2008.

Online.

173

14. Wesley MJ, Hanlon CA, Miller MD, Porrino LJ. Heavy marijuana users show altered

neurofunctional activity during decision making and feedback processing. College on

Problems of Drug Dependence annual meeting, 2008. Online. (oral presentation)

15. Wesley MJ, Hanlon CA, Miller MD, Livengood LB, Laurienti PJ, Porrino LJ. Chronic

marijuana users show altered neural processing during decision making and feedback.

International Cannabinoid Research Society annual meeting, 2008. Online. (oral

presentation)

16. Hanlon CA, Wesley MJ, Torrence M, Porrino LJ. Are there cognitive sequelae to

callosal damage in chronic cocaine users? College of Problems on Drug Dependence

Annual Meeting, June 2009. Online.

17. Wesley MJ, Hanlon CA, Porrino LJ. Chronic marijuana users have decreased

responsiveness to emotionally charged stimuli. College on Problems of Drug Dependence

annual meeting, June 2009. Online.

18. Hanlon CA, Wesley MJ, Porrino LJ. Frontal-striatal connectivity in cocaine users and its

association with behavior: an fMRI. Program No. 158.2, 2009 Neuroscience Meeting

Planner. Chicago, IL: Society for Neuroscience, 2009. Online.

19. Porrino LJ, Wesley MJ, Smith HR, Torrence M, Hanlon CA. Relationship between

motor and cognitive performance in chronic cocaine users. Program No. 158.3, 2009

Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2009. Online.

20. Wesley MJ, Hanlon CA, Porrino LJ. Altered functional responses to emotional stimuli in

chronic marijuana users. Program No. 91.21, 2009 Neuroscience Meeting Planner.

Chicago, IL: Society for Neuroscience, 2009. Online.

Funding and Scholarships

2005-2009 National Institute on Drug Abuse (NIDA) Training Grant

Neuroscience of Drug Abuse Training Program,

T32 DA07246

National Institutes of Health

Childers, Steve

Predoctoral trainee, 100%

1997-2001 Georgia HOPE Academic Scholarship

174

2001 Summer Undergraduate Research Experience (SURE)

Training Scholarship

2000 SURE

Training Scholarship

1997-1999 Ed Marsicano Humanities Scholarship

1999 Phi Theta Kappa Academic Scholarship

EDUCATIONAL ACTIVITIES

Educational Publications

1. Research roundup: Innovative research from today’s psychology graduate students.

Marijuana Users are bad card players. Magazine Article – gradPSYCH 7(2): 10

Teaching

Classroom Instruction

2004 PSY-426 Biological Psychology Guest Lecturer Berry College

2009 PSY-503 Drug Use and Effects Guest Lecturer Unv. South

Carolina

Mentoring

2002 Ms. Loraine Stanley, SURE Scholar, Emory University. Research Assistant

mentor for self-administration methods and analysis leading up to poster

presentation. “Extracellular Levels of GABA in Ventral pallidal sub-regions of

physically dependent rats during response-dependent and response-independent

morphine administration”

175

ORGANIZATIONAL ACTIVITIES

Professional Societies

2009-present Member, College on Problems of Drug Dependence

2007-present Member, Society for Neuroscience

2007-present Member, International Cannabinoid Research Society

Professional Service

2009 Symposium on Drugs of Abuse in Winston Organizer WFU

Salem, NC

2005 Grad700 “Intro to Professional Development” Assistant WFU

2001-2002 Psychology Society President Berry College

2000-2001 Psi Chi Society President Berry College

2000-2001 Psychology Department Faculty Student Rep Berry College

1998-1999 Student Government Association President Bainbridge

1998-1999 Presidential Search Committee Student Rep Bainbridge

1998-1999 Sigma Kappa Delta President Bainbridge

1998-1999 Phi Theta Kappa President Bainbridge

1997-1998 Student Government Association Vice President Bainbridge

1997-1999 Academic Quiz Bowl Team Co-Captain Bainbridge

RECOGNITION

Awards and Honors

2008 Billy R. Martin Award for Outstanding Oral Presentation by a Pre-/Post-Doctoral

Student, 18th Annual Symposium of the International Cannabinoid Research

Society

176

2008 Travel Award: International Cannabinoid Research Society annual meeting

2007 Travel Award: International Cannabinoid Research Society annual meeting

2002 Yerkes National Primate Research Center (YNPRC) Clifford Work Excellence

Award

2002 Henry O. Rollins Work-study Award

1999 Phi Theta Kappa All-Georgia Academic Team Member

1999 Sigma Kappa Delta Humanities Award

1999 Who’s Who

1999 Student Government Association (SGA) Outstanding Service Award

1998-1999 College Bowl Outstanding Player Award in Humanities: Art and Music

1998 Presidential Award: Student of the Year

Invited Talks

2001 May 5 “Handedness in captive chimpanzees: Discovering what is right”

Psychology Lecture Series. Dept. of Psychology. Berry College

2006 September 8 Emory University/Wake Forest University Laboratory Exchange,

Department of Physiology & Pharmacology, Wake Forest University

School of Medicine. Invited speaker.

2006 October 27 “Ineffective neural strategy underlies poor decision-making in chronic

marijuana users”. Carolina Cannabinoid Collaborative. Invited speaker.

2007 September 7 Emory University/Wake Forest University Laboratory Exchange,

Department of Psychology, Yerkes National Primate Center, Emory

University School of Medicine. Invited speaker.

2007 October 13 “Chronic marijuana users show altered neural response to monetary

loss”. Carolina Cannabinoid Collaborative. Invited speaker.

177

2008 September 19 Emory University/Wake Forest University Laboratory Exchange,

Department of Physiology & Pharmacology, Wake Forest University

School of Medicine. Invited speaker.

2009 April 22 North Carolina Parent Network: Education on Drugs of Abuse.

Burlington, NC. Invited Speaker.

2010 April 12 Medical University of South Carolina. Department of Psychiatry and

Behavioral Sciences. Invited Speaker.

2010 May 14 University of Kentucky. Department of Behavioral Sciences. Invited

Speaker.

2010 July 20 Columbia University. Department of Psychiatry