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Running head: TEMPORAL CHOICE IN DEPRESSION AND BIPOLAR DISORDERS
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Temporal Choice in Depression and Bipolar Disorders: Analyzing Amygdala Activity in
Monetary Choice Questionnaires
Elijah J. Fiore
Neuroeconomics
ECON 4454
Abstract
Depression and bipolar disorder are both affect conditions that have been known to affect
decision-making in a multitude of ways, such as the dysregulation risk (Murphy et al., 2001).
However, not much research has been done on the topic of temporal choice in these illnesses.
Due to neural differences in patients with these disorders, there is a possibility that these groups
could react differently in reward delay discounting tasks and show different activation,
specifically in the amygdala (Strakowski et al., 2002). My study aims to combine a monetary
choice questionnaire with fMRI imaging to investigate if bipolar patients discount more heavily
than their depressed counterparts and to connect this with greater BOLD response in their key
structural difference: the amygdala.

TEMPORAL CHOICE IN DEPRESSION AND BIPOLAR DISORDERS
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Introduction
This paper looks at the role of affect disorders such as major depressive disorder (MDD)
and bipolar disorder in decision-making and assigning value, particularly in temporal choice
situations. This is an important topic for investigation as these disorders affect millions in the
United States alone: about 6.7% of adults suffer from depression and 2.6% from bipolar disorder.
Findings could help researchers recognize the specific areas of the brain generally involved in
these types of conditions and may lead to understanding why those affected by these
psychological disorders behave in harmful ways.
Current literature identifies how these affective disorders generally affect the decision-
making ability in patients that could lead to detrimental behaviors. Articles such as “Decision-
making Cognition in Mania and Depression” from Psychological Medicine touch on risk as this
decision-making characteristic. Their experiments found that manic (Bipolar I) patients tended
to make more “bad” decisions in a simple gambling task than depressed and control groups while
both treatment groups both performed worse (earned less points from gambling) than their
control counterparts (Murphy et al., 2001). Murphy and others (2001) showed the apparent
tendency for both affective disorders to influence risky behavior, at least in the context of
gambling tasks when compared to healthy controls.
This study used a gambling task to study risk preference in these subjects; however, I am
precisely interested in studying temporal choice as it applies to manic and depressed patients as
previous research has shown a connection between impulsivity (high discounting) and suicide, a
huge issue for these affective conditions. Up to 15% of those clinically depressed die by suicide
and, in clinical samples, around 50% of patients with bipolar disorder had histories of suicide
attempts. A survey study by Klonsky and May (2010) found a significant correlation between
samples of suicide attempters and relative UPPS impulsivity scale scores compared to ideators
(or those that only thought about suicide) in the form of lack of premeditation. This refers to
suicide attempters not considering consequences in their decision-making, a clear indicator of
high impulsivity (Klonsky & May, 2010).
A study by Dombrovski and others (2012) furthered research by investigating suicide
attempts in the context of delay discounting and temporal choice. The study focused on
alterations to the basal ganglia influencing impulsivity of choice and suicidal tendencies in older
patients. The researchers found lesions in the basal ganglia and striatal region of the brain were

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related to greater delay discounting and its possible correlate, suicide attempts (Dombrovski et
al., 2012). My proposed work will be a natural continuation of this literature, but with the added
dimension of mania (bipolar disorder) and with a younger sample population.
Something of great interest to me in this general topic is if there is any difference
between the conditions in their delay discounting and if this dissimilarity can be discretely
correlated to a specific region of the brain. While not many studies exist on discrepancies in
discounting between these concerning groups, there is literature available on the brain areas
seemingly responsible for temporal decision-making and brain differences between depressed
and manic individuals. In an imaging study by Strakowski and others (2002), MRI found
structural enlargement in the basal ganglia and amygdala regions for bipolar patients; whereas,
for unipolar (depressed) patients, these regions were smaller than in healthy persons. The
amygdala is also implicated in regulating delay discounting in macaque monkeys in a study by
Hirai and others (2009), as well as in rats in its connection to the mPFC in a paper by Churchwell
and colleagues (2009). This makes it an appropriate focus for studying in human subjects in my
proposed neural experiment.
Based on the available research and literature on both disorders, it follows that bipolar
patients will display higher activation in the amygdala during reward delay discounting tasks
compared to depressed subjects. Furthermore, bipolar patients will also discount higher than
depressed patients due to the documented stimuli sensitivity by individuals with mania, perhaps
caused by structural differences in the amygdala. These hypotheses will be tested through a
combination of a delay discounting task and fMRI imaging focusing on the amygdala. The
experimental task will be similar to the monetary choice questionnaire from a paper where heroin
addicts were administered questions revolving time-dependent monetary reward choices over the
course of a year (Kirby et al., 1999). Subject responses will be aggregated in order to develop
discount functions for comparison within and between groups in order to test the secondary
hypothesis. In order to test the primary hypothesis, fMRI imaging will be used during this task
in order to identify any activation differences in amygdala neurons.
While depression affects 6.9% of the US population, that percentage is greater when just
looking at young adults ages 18-22. In addition, developing research shows that the rate of
Bipolar disorder in adolescents is fast approaching the adult rate of 3.9%. It is important to be
able to understand how young adults with these affects perform in the context of temporal choice

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and impulsivity as even without depression and mania, young adults have been shown to have
greater delay discounting (Mies et al., 2016). This is what my experiment aims to do while also
putting an emphasis on the differences in brain activity between these groups.
Background
A bevy of research has be done on decision-making in affect disorders such as bipolar
disorder (often referred to as mania or manic depression) and depression (also known as unipolar
depression and major depressive disorder). This literature highlights the variety of neural effects
on individuals who suffer from these types of conditions in an economic context that guides this
paper’s investigation. A study from Psychological Medicine reveals a clear separation between
healthy populations and affect patients in risk-related valuation in gambling tasks. The authors
found a significant difference in performance between the factor groups (manic and depressed
patients) and matched controls in amount of money earned in a repeated gambling study as these
groups performed more poorly as a whole in terms of selecting more valuable choices and in
decision time (Murphy et al., 2001). Studies like this are important for showing an important
component characteristic of depression and manic persons: risk and the impulsivity that comes
with it.
There are a multitude of papers on the subject of impulsivity, or more specifically
temporal choice and reward delay discounting, as it relates to depression. A study by Mies, De
Water and Scheres (2016) looked at previous work on ADHD and delay discounting as well as
the lack of investigation in depression. The researchers studied any relation between ADHD and
depressive symptoms and preference reversals (the switch from preferring smaller immediate
reward to larger delayed reward when the smaller is also delayed). They also investigated these
symptoms’ correlation to delay discounting of losses. The two studies had undergraduate
students take questionnaires on ADHD and depression symptoms and participate in DD tasks.
The first study had NOW and FUTURE conditions, the NOW condition being small reward now
and large reward in a year while the FUTURE condition had them both delayed a year. The
second study had both gains and losses possible under similar conditions. Participants showed
reversals in both studies and losses were less steeply discounted compared to gains. The authors
concluded that impulsivity in depression cannot be explained by a sensitivity to reward
immediacy (Mies, De Water & Scheres, 2016).

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In contrast to the previous article, another paper looked at temporal discounting in MDD
patients compared to healthy controls in a delay discounting task. The authors found a distinct
group effect on reward discounting and found current MDD patients heavily discounted large
rewards compared to the previously MDD and control groups. They also found a correlation
between discounting and feelings of hopelessness. In addition, current MDD patients were also
found to be less sensitive to differences in reward size between medium and large rewards. The
authors postulated that MDD sufferers do not put much stock into their futures and thus, value
current rewards higher (Pulcu et al., 2014).
A related study from Phenomenology and the Cognitive Sciences saw the researchers use
an interview technique in order to create a comprehensive profile for depressed subjects in terms
of their temporal agility (recognizing change over time, scheduling, treating time as a resource,
etc.) and temporal inability, or the collapse of hope (inability to project one’s self into the future).
The interviewers found a surprising amount of agility even in severely depressed individuals but
heightened temporal inability by the same group. From these investigations, the common thread
in impulsivity in depressed patients appears to be inability to assess the future (whether that be in
hopelessness or by any other cause) (Owen et al., 2015).
A paper in the Journal of Abnormal Psychology found a similar result for bipolar
patients. The authors of this paper aimed to evaluate the delay discounting profiles of Bipolar
and Schizophrenic individuals as compared to healthy controls. Using a delay discounting task
that comprised of hypothetical monetary choice scenarios in a computer environment, the
researchers found the relative indifference points (i.e. the discount functions) of each participant.
The results showed that both non-control groups discounted much more steeply than their
healthy counterparts (Ahn et al., 2011).
Another study looking at bipolar decision making by Richard-Devantoy, Olie, Guillaume
and Courtet (2016) establishes a connection between depression/bipolar disorder and suicide by
performing a meta-analysis of these patients’ performances in an IGT-style task. The trials were
split between groups (unipolar and bipolar) and between suicide attempters and non-attempters.
Researchers found a significant link between decision-making merit and the risk of suicidal
behavior in these subjects. These attempter groups performed worse than their non-attempter
counterparts in the IGT task (Richard-Devantoy et al. 2016).

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Research by Drombrovski and others (2012) also deals with suicide, linking the
established impulsivity from impaired temporal discounting in depressed patients to suicide as
well as incriminating certain brain regions in the older subjects of the study. The article
references how basal ganglia alterations are related to impulsivity of choice and abnormalities in
this area are implicated in previous studies to suicidal behavior. In addition, the paper mentions
unplanned suicide attempts being associated with increased discounting in delayed rewards, a
behavior usually associated with the striatum. The researchers attempted to connect late-life
depression and suicide attempts to increased delay discounting and the basal ganglia. The study
used three groups that tested for major depression (suicidal), depression, and non-depression and
administered money-choice questionnaires and Cambridge gambling tasks while subjects’ brains
were recorded using MRI techniques. The authors concluded in their findings that, after taking
into account small sample sizes, that lesions in the striatal region of the brain contributes to
suicidal behavior by increasing impulsivity (Drombrovski et al., 2012). From these articles, it is
clear that suicide is a possible correlate to impulsivity and, as it is a serious issue plaguing those
suffering with affect conditions, would be interesting to study through temporal discounting in
both unipolar and bipolar populations.
Studying any difference in delay discounting in these groups requires investigating
differences in structure and functioning in the brains. This can aid in isolating areas to study in
temporal discounting tasks. Researchers in a comparison MRI study found less frontal and
prefrontal cortical volumes in both types of patients compared to healthy individuals
(Strakowski, Adler & DelBello, 2002). A study from Cho and others (2014) indicated that these
regions (specifically the medial PFC) reduce delay discounting, or impulsivity, when stimulated
through rTMS (Cho et al. 2014). On the other hand, MRI imaging found structural enlargement
in the basal ganglia and amygdala regions for bipolar patients; whereas, for unipolar patients,
these regions were smaller than in healthy persons. The results of this imaging study indicate a
common underdeveloped/atrophied prefrontal area of the brain between the disorders with the
effect of this being modulated by varying degrees of abnormalities in the subcortical regions of
the brain. If there is a link between amygdala/basal ganglia activity and temporal choice, this
could be a good region to observe in my experimental task (Strakowski et al., 2002).
In a meta-analysis that used fMRI data to separate the disorders, researchers showed how
both disorders usually stem from emotional dysregulation, or the impaired ability to self-regulate

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emotions. This can come from impaired cortical and limbic interactions. The authors discuss
that bipolar disorder often stems from abnormal vlPFC activity, while unipolar disorder
(depression) generally comes from increased dmPFC activity. The researchers also mention how
bipolar disorder increases sensitivity to positive and negative emotional stimuli, which is
consistent with structural changes in the amygdala in these persons (Fossati, 2012).
Work by Hirai, Inoue, Miyachi, and Mikami (2009) looked at the apparent connection
between the amygdala and reward delay discounting as the authors used macaque monkeys to do
single-unit testing on amygdala cells during delayed reward tests. They found that there was
greater neuronal response in a “cue” period before the larger (delayed) reward was selected,
perhaps implicating the amygdala’s role in regulating self-control. The article makes it clear that
a stimulation study with the amygdala seems to be a good choice when comparing temporal
choice between these two populations (Hirai et al., 2009).
Furthering the investigation into the amygdala in relation to temporal choice, a paper by
Churchwell and others (2009) looks at the connection between the prefrontal cortex and the
amygdala to establish functional roles in delay discounting and reversal. The authors explored
the role of the PFC regions in managing impulsivity and self-control while noting the amygdala’s
similar functions. The results showed that rats with a disconnect between the medial PFC and
the basolateral amygdala showed increased impulsivity using a delay-discounting paradigm
(Churchwell et al., 2009).
Looking at the role of the amygdala in human subjects is less definitive. Townsend and
others (2010) used fMRI techniques to evaluate frontal lobe and amygdala activity in non-
medicated MDD patients and matched controls in an emotional face task. Significant PFC and
amygdala activation were seen in both groups. In depressive/anxious individuals, there was less
orbitofrontal activation. In the case of this type of task, there were no clear signs of differences
in amygdala activation in depressive patients compared to healthy controls (Townsend et al.,
2010).
Additional studies, such as the one by Wittfoth-Schardt and others (2015), identify the
amygdala’s connection to temporal choice in humans. The authors were specifically interested in
the neural correlates for delay discounting, specifically in relation to certain personality traits
(mainly anxious-depressive). This study uses fMRI brain imaging to track brain activation in the
mPFC and amygdala during delay discounting tasks. The researchers had patients fill out

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surveys to develop personality profiles to reflect impulsivity and anxiety/depression in order to
analyze the results through these factors. In this task, the authors found more activity in the
mPFC for immediate over delayed reward. Amygdala activation was a correlate for reward
magnitude in immediate reward, but not for delayed rewards. Amygdala activation was higher
for the impulsive patients and mPFC activation was higher in anxious/depressed patients
(Wittfoth-Schardt et al., 2015).
If these disorders show structural differences in the amygdala and both non-human and
human studies implicate the amygdala in self-control and temporal choice, it follows that patients
with affective conditions may perform differently in delay discounting tasks.
Rationale and Hypothesis
Unipolar and Bipolar disorders (depression and manic depression) are unequivocally
linked by the areas of the brain they are associated with. In a meta-analysis of volumetric MRI
case studies of both disorders, researchers found an indication of a common
underdeveloped/atrophied prefrontal area of the brain between the disorders with the effect of
this being modulated by varying degrees of abnormalities in the subcortical regions of the brain,
such as the basal ganglia and amygdala (where bipolar patients generally had enlarged structures
and unipolar smaller structures than their healthy counterparts (Strakowski, Adler & DelBello,
2002). If there were a link between amygdala/basal ganglia activity and temporal choice, this
would be an optimal region to detect differences in temporal choice between the two affective
populations, as well as a possible cause for the results.
Furthering this research, a single-unit study on reward delay discounting in non-human
primates found that when macaques were exposed to a delay-discounting task, the amygdala
showed greater neuronal response in a “cue” period before the larger (delayed) reward was
selected, perhaps implicating the amygdala’s role in regulating self-control (Hirai et al., 2012).
Another study looks at the connection between the prefrontal cortex and the amygdala to
establish functional roles in delay discounting and showed that rats with a disconnect between
the medial PFC and the basolateral amygdala showed increased impulsivity using a delay-
discounting paradigm. This article follows up the previous two as it shows that there is a
connection between the amygdala (which shows structural differences between depressed and
bipolar individuals) and temporal choice, my topic of interest (Churchwell et al., 2009). The

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previous literature on these topics make it clear that a delay discounting study with the amygdala
as the main focus is reasonable when comparing temporal choice between depressed a bipolar
subjects.
The hypotheses I aim to test focuses on the amygdala’s role in reward delay discounting
and the perceived structural differences found by the meta-analysis between unipolar and bipolar
depressive cases (Strakowski et al., 2002). Due to this research, it follows that activation of the
amygdala will be marginally higher in bipolar patients compared to depressed patients in reward
delay discounting tasks. In addition, I predict higher discounting and more impulsivity in the
bipolar subjects due to increased sensitivity to positive and negative stimuli characterized by
bipolar disorder, possibly being caused by the greater activation in the amygdala (Fossati, 2012).
Methods
To test these hypotheses, I propose a simple delay-discounting economic task using a
monetary-choice questionnaire used in previous studies with addicts (Kirby et al., 1999).
Subjects will be administered questions revolving time-dependent monetary reward choices over
the course of a year with the random chance to actually get the money selected based on
performance. This design will ensure subjects reasonably try to answer in an honest and
motivated manner while their choices will be used to calculate a discount equation.
The monetary choice questionnaire will be a series of 27 questions of the following form:
“Would you prefer $X in B days, or $Y in C days,” where the variables represent variants of
dollar amounts and days. These survey responses can be aggregated for each participant in order
to form comprehensive discounting functions that relay important temporal choice qualities like
impulsivity that is important for comparison of within and between groups. The experiment will
use fMRI to track BOLD response in the amygdala to record relative activations during the task.
The experimental procedure will be carried out in 52 weekly runs throughout a single year
between the focus groups and a comparable healthy control group to establish a baseline for
results.
Participants
Ideally for this design, there will be at least 20 subjects in each of the groupings
all above the age of 18 with a preferred mean age of 35 (with some variation allowed
depending on availability). This will ensure a sample suitable for ample testing and

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samples with comparable characteristics that will not introduce unwanted factors into the
analysis like age difference. On the topic of gender, it is preferred if there is a roughly
even split between male and female participants, though a perfect division is not required.
Due to multiple factors involved in the experimental design proposed by this
report, it is crucial to have a screening process for selecting appropriate participants.
Primarily, because of the extensive fMRI imaging process used in the design that requires
relative stillness, I will be using and older subset of subjects (i.e. 18 and up) in order to
make the process as smooth as possible. Additionally, the study I am proposing requires
group with specific conditions (depression and bipolar disorder) so identifying
appropriate patients based on diagnoses is vital to a valid experimental result. All
participants and their consulting physicians will be contacted in order to approve the
details of the experiment and consent to testing to begin the screening process. After this,
each group (depressed, bipolar, and healthy) will be administered diagnostic tests to
validate their grouping. This will include the applicable DSM-V screenings from the
APA, with the same tests being applied to the healthy participants to verify the control
group. Lastly, participants will all be given reading and general intelligence tests in order
to control for these factors and match comparisons.
Materials
With this type of experiment, the main equipment needed is an fMRI machine for
BOLD imaging; however, the system required for this study has to be slightly more
advanced because of its economic task. Due to the readings being done during the
monetary choice questionnaire, there needs to be a digitized version of the survey as well
as a visualization component within the fMRI set-up to facilitate this. This can include a
visual display in the machine itself, if possible, as well as a binary controller to give the
participant to select one of the two choices.
Furthermore, this study follows the Induced Value Theorem in order to evoke
measurable responses characteristic of these disorders. In the task, at each of the weekly
runs, subjects will have a random chance (1 in 27) to receive their chosen reward from
the question in order to convince participants that their choices truly matter. Due to this

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chance of monetary reward, an important material for this study is the money itself to
fund the rewards. Ideally, this would be sourced from a research grant.
Neural Data
The first set of data that will be recorded for each subject is not of neural or
biological nature, but the actual response data from the questionnaire. This will be
tracked through the binary responses to the survey and will simply be representations of
their choices in each definitive option. These choices will be translated into discrete
temporal preferences and, finally, into discount functions for each subject that will aid in
the group-wise and subject-wise comparisons in analysis. The secondary set of data is
arguably much more important and involved in nature; this is the fMRI scans based off
the BOLD responses during the economic task.
The fMRI imaging will be focused on the region of the amygdala, the area of
interest in the study, and will be used concurrently with survey responses. Due to the low
temporal resolution of the measurement technique that can make it difficult to
definitively relate tasks events to BOLD response, survey questions will be displayed 1
minute apart (to act as a buffer between trials) and measurements will be taken during a
set latency period where the subject can see the question but not answer (roughly 5
seconds). This stipulations allow for separation between responses from different survey
scenarios, as well as allowing BOLD response to occur in time for recording. Figure 1
represents the steps of this experimental design as described for the fMRI portion.
Would you prefer $5 today,
or $15 in 10 days?
PLEASE SELECT
A:$5 today
B: $15 in 10 days
PLEASE WAIT
5 seconds 1 minute
Measurement begins
Figure 1. Experimental task display screens including timing and measurement periods.
Measurement ends

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Procedure
First, prospective depressive and bipolar patients are contacted through their
consulting physicians (as described previously) and healthy participants through the
appropriate channels. Each participant is then confirmed for their grouping which
includes DSM-V diagnosis and referring to their medical assessments. Individuals will
also be confirmed to not have any metal plates or pace-maker implants that could cause
issue with the fMRI machine. After the alignment is validated through the screening
process, each participant is then administered a reading comprehension test (Adult
Reading Test, 2nd
edition) and then a simple intelligence test (such as an elementary IQ).
Scores are noted for later reference for each participant.
As a participant arrives for one of their 52 weekly runs of the economic task, they
will be brought to the fMRI imaging room where the task is set up. This room will
include an administrator as well as any technicians necessary to operate the experimental
design. Subjects will be prepped for the fMRI machine (including removing any
magnetic accessories) and made aware of the necessary aspects of the task. This
comprises of a brief overview of their mission in the experiment (i.e. choose your most
preferred bundle), the possibility of reward, as well as any instructions for the devices
used like the remote control for making selections and the display within the machine that
shows each question.
Participants are then placed in the fMRI machine and begin the experimental task.
The display screen shows the first of the 27 survey questions with no instructions for 5
seconds. At this point, fMRI begins to take measurements as the screen changes to show
the 2 options and instructions to select the preferred choice. After the choice is made, a
wait screen is displayed to break up the choices for a minute as fMRI imaging is stopped.
BOLD response data as well as the actual response data are recorded for each of the 27
questions. After the questionnaire is completed, the participant is instructed to collect
any reward they may have accrued in the task, is debriefed on what they chose and the
date of the next run and is free to leave.

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Analysis
Looking at the data collected, there is distinct numerical analysis possible to evaluate
both hypotheses. For the discrete questionnaire responses (as mentioned previously), choices
can be translated to discount functions and impulse profiles based on said functions. If bipolar
patients discount future rewards higher than depressed, then this secondary hypothesis is
confirmed. These discounting behaviors can be compared between groups using analysis of
variance, looking at differences in group means as well as in subjects matched in certain
characteristics like age, reading score, etc.
The data from the fMRI imaging is considered within a generalized linear model
framework in which the hemodynamic response (BOLD) at each point in time is a sum of all
events at that point. This real time data is placed into a design matrix that separates points into
voxels and, when visualized, returns an image where more activity results in a brighter region.
The GLM model is used to predict HDR values for each point of the brain at every time point
and these values, specifically for a region of interest like the amygdala, can be interpreted and
compared. In order to accept our hypothesis, there must be a positive significant difference
between the voxel activation (HDR) in the amygdala regions of the brain for bipolar patients
compared to depressed patients. This would definitively show higher neuronal activity in this
region for bipolar patients that could, in turn, lead to causal analysis using manipulation
techniques in the future.
In order to possibly create a better understanding of these diseases compared to a healthy
norm, all of these results can be statistically compared to the control group data.

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References
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Cho, S. S., Koshimori, Y., Aminian, K., Obeso, I., Rusjan, P., Lang, A. E. Strafella, A. P.
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