Running head: NEUROECONOMICS OF SUGAR€¦ · Running head: NEUROECONOMICS OF SUGAR 4 cognition and attention, as seen in hypoglycemia (Mergenthaler et al., 2014). Increased insulin

Running head: NEUROECONOMICS OF SUGAR

The Neuroeconomics of Sugar:

Effects of Glucose and Sucralose on Reward Signaling

Vanya Vojvodic

NEUR-494 Neuroscience Honors Thesis

Principal Investigator: Dr. John Monterosso

Thesis Advisor: Dr. Irving Biederman

University of Southern California

Fall 2019

Running head: NEUROECONOMICS OF SUGAR 1

Abstract

The body’s metabolic state has been shown to impact decision-making and reward-signaling in

the brain, although the associated mechanism is poorly understood. Brain activation patterns

have been found to differ in states of hunger compared to states of satiety, with caloric

deprivation associated with a lowered reward threshold in intracranial self-stimulation animal

models (Carr, 2002). This study continues to explore how incentive-signaling changes as a

function of metabolic state through neuroimaging, specifically by analyzing associated brain

activity with glucose and sucralose sugar consumption within the Monetary Incentive Delay

(MID) task model. Healthy participants (N= 32) completed the MID task in an fMRI scanner

during three neuroimaging sessions. On each trial of the task, participants were cued regarding

what they could win, and then waited for a target to appear. The speed of response to that target,

via a button-press, determined their chance of obtaining the prize, either an immediate or delayed

reward of varying magnitude. Each session involved the participants consuming a drink with

water, glucose, or sucralose. Subsequent analysis showed that participant reaction time was

significantly faster for more immediate rewards (p = 0.038) and larger rewards (p = 0.022).

There was no significant effect (p = 0.166) based on the drink condition. Whole brain analyses

revealed significant activation differences for reward magnitude and immediacy contrasts across

all sugar conditions. Region of interest analyses showed that activation contrasts across all sugar

conditions revealed significant differences for reward magnitude and immediacy within the

nucleus accumbens, caudate, putamen, thalamus, and insular cortex. Between glucose and water

drinks, however, there was no evidence of a metabolic state interaction with varying reward

conditions. Though preliminary, these results do not support the primary study hypothesis.


Introduction

Our current understanding of the specific pathways in which the body’s metabolism alters

behavior is not well understood. Metabolic state has been shown to impact decision-making and

reward-signaling in the brain. Acute caloric deprivation has been associated with heightened

food cue responsivity, potentially due to mechanisms involving ghrelin, glucose, insulin, and

glucagon-like peptide-1 (GLP-1). Ghrelin levels increase during food deprivation and have been

associated with food-seeking behavior and increased dopaminergic signaling in the nucleus

accumbens (Diepvens et al., 2008; Nogueiras et al., 2004). Circulating levels of glucose, GLP-1,

and insulin levels increase after food intake and have been shown to alter reward sensitivity, with

elevated reward thresholds resulting in intracranial self-stimulation rat models after insulin and

leptin infusion (Bruijnzeel et al., 2011; During et al., 1995).

Various fMRI paradigms have been implemented to examine neuroanatomical sites

sensitive to metabolic processes. Findings in human studies indicate that central nervous system

(CNS) activity varies based on satiety and food or non-food reward presentation (Fuhrer et al.,

2008). Activation patterns differ when a person is in a state of hunger versus a state of satiety in

regions including the left anterior cingulate cortex, right amygdala, extrastriate cortex, and

orbitofrontal cortex (Fuhrer et al., 2008). Food rewards resulted in increased CNS activity in the

insula, left striate, extrastriate cortex, thalamus, anterior mid-prefrontal cortex (Fuhrer et al.,

2008). With nonfood reward images compared to food images, there were activation changes in

the right parietal lobe and left and right middle temporal gyrus (Fuhrer et al., 2008). Through

fMRI studies, hunger has been shown to interact with attention and food reward processing (Siep

et al., 2009). Participants that experienced food deprivation showed increased reward processing,


denoted by increased brain activity in mesocorticolimbic structures, following the presentation of

high calorie foods, while those that were satiated showed increased reward processing following

the presentation of low calorie foods (Siep et al., 2009). Specifically, brain activity within the

posterior cingulate cortex, medial orbitofrontal cortex, insula, caudate, putamen, and fusiform

gyrus -- which comprise a large portion of the reward system in the brain -- was different for

individuals who fasted compared to those who were fed (Siep et al., 2009). Since acute fasting

was shown to increase the salience of food cues and alter reward signaling, there may be a

relation between reward signaling and decision-making.

Adding on, research has shown that chronic food restriction in rats increases

drug-induced reward signaling (Carr, 2002). The rewarding effects of cocaine, amphetamine, and

other opioid agonists are enhanced in food-deprived rats, coupled with observations of increased

drug self-administration in this group (Carr, 2002). Metabolic state changes have been associated

with modified reward signaling, as evident by fMRI activation patterns, and behavioral or

self-report impulsivity in various decision-making models. The purpose of this study serves to

give insight on how behavior and brain activity changes as a function of metabolic state,

specifically in regards to the effects of sugar consumption on incentive-signaling in subcortical

brain structures.

Two specific sugars explored in this study were glucose and sucralose. Glucose is a

caloric sugar that circulates within the blood and triggers the glucose-insulin pathway to

modulate blood sugar levels. Glucose is essential to brain metabolism; neurons have high cellular

energy demand and their abundance in the brain makes it the most energy-demanding organ

(Mergenthaler et al., 2014). Physiological disruption of glucose metabolism can lead to reduced


cognition and attention, as seen in hypoglycemia (Mergenthaler et al., 2014). Increased insulin

release, a function of increased blood plasma levels of glucose, has been implicated in decreased

mesolimbic network excitability. This is evident with studies that involved microinjections of

insulin into the ventral tegmental area (VTA), as reward signaling via dopaminergic neurons was

attenuated (Labouèbe et al., 2013). A study conducted by Wang and Huangfu (2017) revealed

that glucose consumption altered decision-making behavior as well. A negative correlation

between blood glucose levels and delay discounting was found, further supporting that glucose

consumption attenuated reward signaling. Different doses of glucose were shown to affect delay

discounting in a non-linear manner (Wang & Huangfu, 2017). With xylitol consumption, which

triggers a non-insulin sugar pathway, there was not the same degree of reward signal attenuation

as with glucose consumption (Wang & Huangfu, 2017). This exemplified how delay discounting

reductions were a function of glucose consumption, as not all types of sugars induced significant

changes in decision-making and energy budgeting.

Sucralose is a non-caloric, high intensity sugar that does not induce blood glucose level

changes nor stimulate the glucose-insulin pathway. The synthetic sweetener is commonly

advertised as being a “healthier” sugar substitute and it is about 600 times sweeter than glucose

by volume (Erbas et al., 2018). It is commonly believed that sucralose is not broken down by

digestive enzymes, but recent studies have contradicted that claim by showing potential

sucralose metabolites. Studies applying thin-layer chromatography to methanolic fecal extracts

of rats and humans following oral sucralose administration have revealed multiple chemical

peaks, suggesting that sucralose may actually be metabolized in the gastrointestinal tract

(Schiffman & Rother, 2013). The metabolic fate of sucralose and the corresponding safety


profile of its metabolites is still unknown. For simplicity, the main focus of this report is on the

metabolic incentive-signaling induced by glucose relative to water consumption.

To compare reward signaling due to sugar consumption, models analyzing incentive-

signaling were utilized. Specifically, the well-established Monetary Incentive Delay (MID) task

was employed in this study. The MID task typically presents an incentive cue to a participant and

then requires them to react as quickly as possible to a stimulus in order to win or avoid losing the

earlier presented reward. The motivation of the participant is associated with the speed at which

the participant responds to the stimulus; this cue response is recorded as reaction time data.

These reaction times serve as behavioral correlates of incentive (Luo et al., 2009). The variant of

the MID task used in this study presents reward levels that differ in both magnitude and

immediacy. Both delayed and immediate monetary rewards are presented individually for

participants to attempt to win, rather than being presented as a forced choice between

binary-choice pairs, as is typically found in other delay discounting paradigms. The MID task

allowed for fMRI imaging data during anticipatory periods to be collected, as well as participant

reaction times to a cue after reward presentation. This allows for the isolation of brain activity

associated with incentive processing to obtain rewards. The specific regions of interest in this

study during the anticipatory period included: the midbrain, insula, supplementary motor area

(SMA), putamen, caudate, thalamus, and nucleus accumbens.

A study conducted by Knutson and colleagues (2000) first introduced the MID task as a

tool to study whether activity in human striatal and medial forebrain structures reflected the

incentive for the processing of monetary rewards and punishments. Incentive-driven behavior

was associated with corresponding activity in the insula, caudate, putamen, medial PFC, and


other ventral tegmental area (VTA) targets (Knutson et al., 2000). Due to the small size of the

VTA, terminal regions of the VTA were focused on in this fMRI study due to easier detection

through neuroimaging (Knutson et al., 2000). Midbrain dopaminergic neurons were speculated to

contribute to regionally specific activation patterns, and BOLD fMRI data served as an indirect

index of dopaminergic signaling. Another MID task study found that participants have faster

reaction times, or cue response times after reward presentation, to immediate rewards relative to

delayed rewards and a faster reaction time to higher magnitude rewards as opposed to those of

lower magnitude (Luo et al., 2009). A greater response in regions sensitive to incentive values,

such as the anterior insula and putamen, was induced by more immediate rewards (Luo et al.,

2009), which mirrored the activity patterns from earlier studies assessing monetary reward

(Knutson et al., 2000). The MID task allows researchers to examine neural activity that

corresponds to different levels of reward processing, such as during reward anticipation or

reward consumption phases. Additionally, the specific MID task variant used in this study is

unique as it examines underlying impulsive tendencies, as there is no direct decision-making

component between the rewards in which self-control mechanisms may mask behavior.

Other MID task studies continued to investigate the neural basis of reward anticipation

and consumption. An fMRI study conducted by Rademacher et al. (2010) analyzed how brain

activity varied with monetary and social rewards as well as during the anticipatory reward phase

and consumption phase. The reward anticipation phase activated brain structures in the ventral

striatum, nucleus accumbens, caudate, and putamen in a manner that was independent of the

reward type (Rademacher et al., 2010). The consumption phase, however, had reward-specific

activation, with changes in amygdala activity for social rewards and changes in the activity of the


thalamus for monetary rewards (Rademacher et al., 2010). The breadth of brain activity during

the anticipatory phase across multiple reward types suggests that this neural network is

implicated in a variety of reward-seeking behaviors. By gaining insight into the activity of the

anticipatory network, more specific, downstream consumptive reward-processing can then be

manipulated.

The manner in which addictive behavior is linked to changes in reward processing was

also studied using the MID task and delay discounting. An inverse correlation between

anticipatory striatal activity and trait impulsivity within drug users was found, but this

relationship is not consistent (Balodis & Potenza, 2014). Divergent signaling in the ventral

striatum during the anticipation phase has been observed in participants with substance use

disorder (SUD) and in high-risk subject populations, but signal variations persist across SUD

populations and their individual characteristics (Balodis & Potenza, 2014). Delay discounting is,

on average, greater among populations with substance use disorder (SUD) than in control groups

(Lutz & Widmer, 2014). Subjects with high reward sensitivity presented with increased activity

in the nucleus accumbens during reward anticipation phases in the MID task (Lutz & Widmer,

2014). The MID task is a highly flexible paradigm that can be applied to study a variety of

reward processing mechanisms and how they relate to addictive behaviors.

These prior studies, most notably Knutson et al. (2000) and Luo et al. (2009), served as a

model for the implementation of the MID task in this study to examine subcortical reward

signaling in response to metabolic manipulation with various sugars. A focus on the previously

defined regions of interest (midbrain, insula, SMA, putamen, caudate, thalamus, and nucleus

accumbens) stem from the activation patterns observed in these prior studies. The purpose of this


study is to gain a better understanding of how food consumption alters impulsivity and reward

signaling. By comparing participant MID task performance between glucose and sucralose

metabolic states relative to a water control, changes in brain activity patterns and impulsivity can

be observed in regards to reward magnitude and reward immediacy.

Across the four reward anticipation conditions, it is hypothesized that reward signaling in

the brain will be attenuated in both sugar conditions relative to the water (control) condition. The

primary focus of this thesis is to compare reward signaling change between glucose and water

conditions. An acute sucralose manipulation was also implemented in this study as a non-caloric

sweetness match, and the consumption of glucose is hypothesized to have a greater degree of

reward signaling attenuation than sucralose. The sucralose manipulation, however, will not be

included in this analysis. These manipulations may also have special relevance to the immediacy

of the reward presentation, but there is limited statistical power for this analysis due to the

sample size. As a secondary hypothesis, it is predicted that sugar consumption will show less

differentiation between immediate versus delayed rewards relative to the water condition. The

glucose condition is hypothesized to have a greater degree of immediacy effect attenuation than

sucralose as well. This study serves to provide insight into how sugar consumption can alter

incentive-signaling and reward sensitivity in the brain.

Methods Contribution

My personal contribution to this study included assisting with data collection, data

analysis, and the subsequent discussion write-up. I recruited participants, trained participants on

the behavioral study tasks prior to scanning, and assisted with running the fMRI scanning


sessions. This included setting up scanner parameters, conducting safety screenings on all

participants, and troubleshooting technological issues regarding the scanner system as well as

with the task programmed in Matlab. I co-conducted approximately 20 fMRI scanning sessions

throughout the study duration. I also played a large role in data organization by converting all

participant files into BIDS format for data analysis. In regards to data analysis, I completed the

statistical analyses of reaction time data obtained from participant responses to cue presentation

in the MID task. Additionally, I contributed to fMRI preprocessing by manually classifying ICA

components as signal or noise for 10 participants across each task trial for each scanning session

(total of six task-related acquisitions per participant), for a total of about 2700 components. This

allowed for a comparison between manual denoising and automatic signal classification methods

through FSL’s FIX software. After the preprocessing stage, I worked with Milad Kassaie, a

doctoral student in the Addiction and Self Control Lab, to conduct the whole brain and region of

interest analyses that are presented in the final results section.

Participants

The participants in this study were recruited from a separate, ongoing Parent Study led by

Dr. Kathleen Page. The inclusion criteria for participant enrollment in the fMRI study, matched

to the Parent Study, stated that participants be 18-45 years old, in general good health with

normal-to-corrected vision, right-handed, fluent in English, and that participants should have no

history of diabetes or other major health problems. Participants also had a BMI of 19-40 kg/m^2

and a fasting glucose level less than 126 mg/dl, as determined by the Parent Study. The final

sample included 32 participants that completed all three scanning sessions in the study, with 13

female participants and 19 male participants. The average age of participants was 24 years old


(min = 18; max = 34; sd = 4.41). One participant dropped out of the study after the first scanning

session. Female participants underwent scanning during the follicular phase of their menstrual

cycle to complement the endocrine measures of the Parent Study. All participants signed a

written informed consent. All procedures were done in accordance with the human subjects study

policies of the USC Institutional Review Board. All participants received a $50 payment per

session, along with bonus payments based on the Monetary Incentive Delay (MID) Task

performance and a study completion bonus of $25.

Experimental Paradigm

Participants had three scanning sessions in an fMRI scanner at the Dana and David

Dornsife Neuroimaging Center at USC. The day before each scanning session, participants were

asked to fast overnight for at least 12 hours. Before each session, participants completed a

practice trial in a separate room outside of the scanner, to practice the MID task they would be

completing in the scanner. Fifteen minutes before scan time, participants ingested a 300 mL

drink of cherry flavored water mixed with either 1) 75 g of glucose, 2) 0.24g of sucralose, or 3)

no additional ingredient (just water and the cherry flavoring). The sucralose drink utilized in this

study was matched in sweetness to the glucose drink. Each drink was ingested within 2 minutes

and study subjects were blind to the conditions. The drink order for each set of scanning sessions

was balanced using a computer-generated sequence. Participants completed online surveys

assessing hunger, satiety, fullness, and sweet and savory food cravings three times during each

session: before drink ingestion, after drink ingestion both before scanning, and after the scanning

session. The surveys utilized a sliding scale with a range of 0 to 100 for different behavioral


ratings of hunger as well as scales ranging from “very slightly or not at all” to “extremely” to

gather information about current feelings and emotions.

Participants completed multiple tasks in the fMRI scanner, including the Monetary

Incentive Delay Task (MID Task). This is a validated method to examine brain activity

associated with reward processing and has been implicated in multiple studies (Knutson et al.,

2000; Lutz & Widmer, 2014). In each session, participants completed two full blocks of the MID

task, each lasting about 7.5 minutes. Each block consisted of 40 trials. Upon task administration,

participants first saw a “+” fixation on the center of the screen for a jittered duration with mean

of 2 seconds. Next, a possible monetary reward of either $5 or $25 was shown on the screen for

about 3.5-4.5 seconds. If the reward images were “closer” (appeared larger on the screen), then

this represented an immediate reward. If the reward images were “further” (appeared smaller and

more distant on the screen), then this represented a delayed reward. The reward presentation

served as the reward anticipation and incentive period. There were four reward levels

(combinations) in the MID Task. When a blue “X” appeared on the screen, the participant was

instructed to respond as quickly as possible by pressing a button. It was explained to the

participants that if they were fast enough with their response to the cue, then the money reward

displayed could be a potential bonus payment of the study. During the reward anticipation

period, a jitter of 3.5-4.5 seconds was used to prevent participants from guessing cue time

placement. The outcome was predetermined, however, with the winning rate for the participant

set to be at 60%. If the participant took longer than 0.5 seconds to respond, then they would lose

the reward payout potential for certain. After each run, participants were given feedback about

their performance. For example, if the participant won $5 on that trial, the feedback would be:


“Win $5 today!” or “Win $5 in 1 month!” for immediate and delayed reward conditions,

respectively. If the participant did not win on a trial, then the feedback would be: “Too Slow! No

Reward”, as exemplified in Figure 1.

Figure 1. Visualization of the MID Task

Participants viewed a screen with a “+” fixation, then one of five reward conditions appeared on

the screen. Reward combinations included either $5 or $25 that were obtainable either today or

one month in the future. Next, a blue “X” appeared on top of the reward presentation, which

prompted participants to respond by pressing a button as quickly as possible. Afterwards,

feedback on participant performance was given on the screen. This was repeated 40 times for

each block of the MID task.

Image Acquisition

Neuroimaging data was collected at the Dana and David Dornsife Cognitive

Neuroscience Imaging Center at USC. A Siemens 3T MAGNETOM Tim/Trio scanner was


utilized to collect functional and structural participant brain data. Within the scanner, participants

laid in a supine position and were fitted with a head device that allowed participants to view the

testing screen with the MID task through a mirror. Blood oxygen level-dependent (BOLD)

responses were measured via the fMRI scanner with an echo planar imaging (EPI) sequence and

with prospective acquisition correction (TR=2s, TE=25ms, flip angle=90 degrees,

resolution=3mm 3 isotropic, 64 x 64 matrix in FOV=192mm). A total of 41 axial slices, each 3

mm in thickness, were acquired in a tilted manner in order to align the slices with each

participant’s AC-PC plane to minimize orbitofrontal cortex signal dropout. A T1-weighted 3D

magnetization was used to collect anatomical images, with an MPRAGE of TI=900 ms and

TR=1.95 s. The TE used was 2260 ms along with a flip angle of 90 degrees. The resolution was

mm, with a 256 x 256 matrix in FOV=256mm. Each individual's structural space was aligned13

to a standardized space.

Image Preprocessing and Registration

FMRI data processing was carried out using FEAT (FMRI Expert Analysis Tool) Version

6.00, part of FSL (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). Registration of the

functional data to high resolution structural image was carried out using FLIRT (Jenkinson &

Smith, 2001; Jenkinson et al., 2002) with boundary based algorithm, or BBR (Greve & Fischel,

2009). Registration from high resolution structural to standard space was then further refined

using FNIRT nonlinear registration with 12 degrees of freedom and a warp resolution of 10 mm

(Andersson et al., 2007a; Andersson et al., 2007b). Other pre-statistics processing included

motion correction using MCFLIRT (Jenkinson et al., 2002); non-brain removal using BET

(Smith, 2002); spatial smoothing using a Gaussian kernel of FWHM (full width at


half-maximum) 4.0mm; grand-mean intensity normalisation of the entire 4D dataset by a single

multiplicative factor; highpass temporal filtering (Gaussian-weighted least-squares straight line

fitting, with sigma=50.0s).

MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent

Components) (Beckmann & Smith, 2004) was used to linearly decompose each subject’s fMRI

functional image into different temporal and spatial components for exploratory Independent

Component Analysis, or ICA. The analysis of the source components of the multivariate signal

allowed for denoising procedures to be implemented in order to differentiate between noise

signals and neural signals related to the study task. The variance associated with components

consisting of noise signals was then removed.

In order to remove noise artifacts from the neuroimaging data, FMRIB's ICA-based

X-noiseifier (dubbed FIX) was utilized as part of the preprocessing protocol. FIX provides an

automatic algorithm using machine learning to reliably differentiate between noise and task

signals, with about a 95% overall accuracy (Salimi-Khorshidi et al., 2014). In order to classify

and denoise ICA components, FIX consists of spatial ICA via MELODIC, spatial/temporal

feature estimation, classifier training by manual component labeling and application to new

datasets (Salimi-Khorshidi et al., 2014). Studies have shown that FIX performance compares

well against manual ICA preprocessing. Data for all participants was processed with the FIX

component of the FSL package, trained on a prelabeled “standard” dataset included with the FIX

package, with a threshold of 20, discarding all signal components below a 20% probability of

being signal. “Cleaned” functional images outputted by FIX, with all under-threshold noise

components removed, were used for statistical analyses of fMRI results.


In addition to using automated ICA techniques to denoise fMRI data, visual inspection of

the independent components was also executed, which is considered a standard method of

denoising (Kelly Jr. et al., 2010). Approximately 40-50 component outputs were labeled per run

as noise or signal based on the associated spatial maps, time-course graphs, and power-spectrum

graphs. Spatial maps were analyzed on the basis of signal localization, as signals in white matter

areas, sinuses, ventricles, or on the outer edges of the brain are commonly associated with noise.

The time-course graphs were analyzed for any large spike responses, especially those greater

than 5 standard deviations in amplitude, and for saw-tooth patterns that were characteristic of

noise signals. The power-spectrum graphs were analyzed in relation to what proportion of the

power was above 0.1 Hz, as this would relay the presence of noise signals if more than 50% of

the power range was over this part of the frequency spectrum. Common sources of noise include

motion artifacts from subject head movement, physiological artifacts from cardiac and

respiratory processes, as well as intrinsic MRI scanner noise (Kelly Jr. et al., 2010). Manual

classification of noise and signal was compared with automatic classification to validate FIX

results for 10 subjects; the results were similar, making type II errors unlikely.

Preprocessing

The fMRI preprocessing phase involved the analysis of individual trial ICA components

in order to remove variance associated with noise. Participants had 40-50 components associated

per run, with the amount of components being identified as noisy signals variable for each run

and participant. Figure 2 and Figure 3 exemplify noise signals and task signals, respectively, in

two different components for the same participant and within the same MID task run.


Figure 2. ICA Component Analysis of a Noisy Signal

This figure displays the spatial map, time-course series, and power-spectrum of a noisy signal.

The spatial map shows “slabbing”, where large areas of activation are present that disregard

standard gray matter/ white matter boundaries. This is typical of head motion artifacts. The

time-course series contains a distinct saw-tooth pattern, along with multiple major spikes that

exceed the 5 standard deviations limit imposed by other denoising standards of analysis. The


power-spectrum also has a majority of its power over 0.1 Hz, therefore further supporting the

presence of noise.

Figure 3. ICA Component Analysis of a Task Signal

This figure displays the spatial map, time-course series, and power-spectrum of a task-related

signal. The spatial map shows consistent areas of activation that respect anatomical boundaries.


The time-course series contains broader peaks that do not exceed the 5 standard deviation limit.

The power-spectrum also has a majority of the area of its power under 0.1 Hz, therefore further

supporting the presence of a signal.

fMRI Analysis

Time-series statistical analysis was carried out using FILM with local autocorrelation

correction. The results of the preprocessing step were submitted to a GLM to analyze the effects

of experimental regressors on blood oxygen level-dependent (BOLD) responses. The first-level

fMRI analyses, containing within-subject comparisons, were performed in native subject space.

The resulting statistical maps were transformed into standard space before higher-level (group)

analyses were executed. The transformation into standard space was performed in two steps:

echo planar images were first aligned to each participant's own structural scan, and then the

image was normalized into standard space [Montreal Neurological Institute (MNI)] using affine

transformations (Jenkinson & Smith, 2001).

The primary aim of our analyses was to study changes in brain activation during the

anticipation period. The time series model included 6 events: four events during the anticipation

period covering presentation of immediate-big, immediate-small, delayed-big, or delayed-small

potential rewards, and two events during the feedback period representing win or loss

occurrences. All events were convolved with a double-gamma hemodynamic response function,

and temporal derivatives were added as null covariates. Null events were left unmodeled. At the

second level of analysis, a fixed effects model was used to average contrast estimates across the

two runs of each experimental condition within each subject, by forcing the random effects

variance to be zero in FLAME (FMRIB’s Local Analysis of Mixed Effects). Results were carried


over to between-subject group analyses, by using FLAME stage 1 and an additional outlier

deweighting step (Beckmann & Smith, 2004; Woolrich et al., 2001). Z (Gaussianized T/F)

statistic images were thresholded non-parametrically using clusters determined by Z >2.3 and by

a (corrected) cluster significance threshold of P=0.05 (Worsley, 2001).

Region of Interest Analyses

Region of interest (ROI) analyses were performed with a priori regions, stemming from

previous findings with the MID task (Knutson et al., 2000; Luo et al., 2009). Selected ROIs were

defined based on FMRIB's Integrated Registration and Segmentation Tool (FIRST), a tool used

to classify subcortical structures. Specifically, the bilateral putamen, caudate, thalamus, and

nucleus accumbens were defined as probabilistic ROIs utilizing the Harvard-Oxford Subcortical

Structural Atlas. Based on the coordinates used by Luo et al. (2009), an additional ROI was

created to include the anterior insula.

Whole Brain Analyses

Whole brain analyses were performed in addition to the ROI analyses. Three major group

comparisons were completed in regards to reward presentation and reward obtainment, across all

drink conditions. The group contrasts were between the differences of activations during the

anticipation of larger vs. smaller rewards, sooner vs. later rewards, and lastly the differences

between winning vs. losing the rewards presented. The Harvard-Oxford Subcortical Structural

Atlas was utilized to identify the structures associated with the maximum cluster Z-scores.

Results Response Time Analysis

Prior to response time analysis, data cleaning consisted of removing response time data

that was below 0.1 s or above 0.5 s. Response times that were faster than 0.1 s were indicative of


a response prior to the cue presentation and response times over 0.5 s were too slow (this is the

maximum time allotted for participant response in the MID task), suggesting that participants

were distracted. Additionally, participant responses were removed if the response time deviated

greater than 3 standard deviations from the participant’s average response time. Responses were

also truncated if they were less than 80 ms or greater than 500 ms. Plot distributions of the

response time data in relation to drink condition, reward magnitude, reward immediacy are

presented in Figure 4, Figure 5, and Figure 6, respectively.

Figure 4. Response Time Data with Drink Condition

The response times (in seconds) were obtained based on drink condition of either water, glucose,

or sucralose. The box plot displays the median and interquartile range (IQR), with the whiskers

extending to the largest and smallest data value collected within 1.5 * IQR added to or subtracted

from the upper and lower quartiles, respectively. The outliers are represented as small circles.


Figure 5. Response Time Data in Relation to Reward Magnitude

The response times (in seconds) were obtained in regards to reward magnitude within the MID

task, with either a smaller reward or a larger reward.

Figure 6. Response Time Data in Relation to Reward Immediacy

The response times (in seconds) were obtained in regards to reward immediacy within the MID

task, with either a sooner reward or a later reward.


Additionally, a 3 x 2 x 2 repeated measures ANOVA was run in order to assess the main

effects of drink condition (metabolic state manipulation of a water, glucose, or sucralose drink),

reward magnitude ($5 or $25), and reward immediacy (reward received today or in one month).

As Figure 7 (Tests of Within-Subject Effects) shows, response time was significantly faster for

more immediate rewards (p = 0.038, ηp² = 0.128) as well as for larger rewards (p = 0.022, ηp² =

0.154). There was no significant effect, however, based on the drink condition of the participants

(p = 0.166, ηp² = 0.055). There was not a significant interaction between drink condition and

reward magnitude (p = 0.552, ηp² = 0.018) nor between drink condition and reward immediacy (p

= 0.495, ηp² = 0.022). Although reporting omega-squared values would yield less biased

estimates of effect size, the statistical analyses for this thesis report partial eta-squared values.


Figure 7. Tests of Within-Subjects Effects via Repeated-Measures ANOVA


Whole Brain Analyses

Figure 8. Threshold Activation Contrasts for Larger vs. Smaller Rewards

Activation contrasts for brain regions for larger vs. smaller rewards revealed significant

activation in the left cerebral cortex and right cerebral cortex (Table 1). These brain regions were

identified by the Harvard-Oxford Subcortical Structural Atlas.

Table 1. Whole Brain Analysis Results for Larger vs. Smaller Rewards

Cluster Voxels P Value Z Max Max X Max Y Max Z Subcortical Brain Regions

4 48402 0 12 -12 -84 -14 Left Cerebral Cortex

3 1711 7.01e^-25 5.43 26 48 24 Right Cerebral Cortex

2 1509 8.27e^-23 5.41 -28 52 30 Left Cerebral Cortex

1 138 0.000665 4.42 -12 -22 36 Left Cerebral Cortex


Figure 9. Threshold Activation Contrasts for Sooner vs. Later Rewards

Activation contrasts for brain regions for sooner vs. later rewards showed significant activation

in the left and right cerebral cortex, left caudate, right putamen, and right pallidum (Table 2).

These brain regions were identified by the Harvard-Oxford Subcortical Structural Atlas.

Table 2. Whole Brain Analysis Results for Sooner vs. Later Rewards


7 7231 0 10.2 8 -88 0 Right Cerebral Cortex

6 573 1.32e^-11 4.91 2 10 66 Right Cerebral Cortex, Left Cerebral Cortex

5 214 1.41e^-05 4.69 -12 8 6 Left Caudate

4 167 0.000137 4.33 18 6 -4 Right Putamen, Right Pallidum

3 156 0.000241 4.58 46 54 -2 Right Cerebral Cortex

2 96 0.00684 3.98 42 20 -4 Right Cerebral Cortex

1 77 0.0225 3.56 46 6 36 Right Cerebral Cortex


Figure 10. Threshold Activation Contrasts for Win vs. Loss Reward Outcome Conditions

Activation contrasts for brain regions for win vs. loss outcome conditions showed significant

activation in the left and right cerebral cortex, left hippocampus, left accumbens, left putamen,

and left pallidum (Table 3). These brain regions were identified by the Harvard-Oxford

Subcortical Structural Atlas.


Table 3. Whole Brain Analysis Results for Win vs. Loss Reward Outcome Conditions


17 4969 0 7.42 -12 8 -8 Left Accumbens, Left Putamen, Left Pallidum

16 4762 0 7.29 -26 20 58 Left Cerebral Cortex

15 4037 0 8 -32 -72 48 Left Cerebral Cortex

14 2435 9.39e^-33 7.07 34 -70 52 Right Cerebral Cortex

13 2299 1.73e^-31 6.73 56 -56 -12 Right Cerebral Cortex

12 2078 2.25e^-29 6.23 -4 -38 34 Left Cerebral Cortex

11 1613 1.15e^-24 7.17 -54 -48 -8 Left Cerebral Cortex


9 900 2.04e^-16 5.66 4 -36 58 Right Cerebral Cortex



6 176 6.51e^-05 4.36 56 -4 -20 Right Cerebral Cortex

5 167 0.000103 4.36 62 -6 -2 Right Cerebral Cortex

4 142 0.000391 5.1 -4 12 24 Left Cerebral Cortex

3 96 0.00569 4.4 -54 -34 12 Left Cerebral Cortex

2 89 0.00884 4.75 -28 -38 -12 Left Cerebral Cortex, Left Hippocampus

1 87 0.01 4.89 -26 28 -18 Left Cerebral Cortex

At present, whole brain analyses separating drink condition have not been completed. However,

we do examine the effect of drink condition in the ROI analyses below.


Region of Interest Analyses

ROI analyses included brain regions of the nucleus accumbens, caudate, putamen,

thalamus, and insular cortex. Beta values were extracted for three contrasts separately.

Activation pattern contrasts in regards to reward magnitude, reward immediacy, and reward

outcome were analyzed across all sugar conditions, as expressed in Table 4 with the mean and

standard deviation for each brain region. Overall anticipatory effects were also analyzed across

all sugars relative to baseline activation levels. More specifically, activation contrasts between

glucose and water conditions were analyzed for reward magnitude, immediacy, and outcome

differences (see Table 5). The same brain areas of the nucleus accumbens, caudate, putamen,

thalamus, and insular cortex were analyzed.

Table 4. Activation Contrast Main Effects Results Across All Sugar Conditions

Main Effects Results Across All Sugar Conditions

Overall Magnitude (Larger-Smaller)

Overall Immediacy (Now-Later)

Overall Outcome (Win-Loss)

Overall Anticipatory (Across all - Baseline+)

Nucleus Accumbens

0.0275 ± 0.1117* 0.0261 ± 0.0791 ** 0.0834 ± 0.0984 ** -0.063 ± 0.1095**

Caudate 0.0458 ± 0.0982** 0.0301 ± 0.0743 ** 0.0161 ± 0.0925 -0.0209 ± 0.1176

Putamen 0.0362 ± 0.0886** 0.0259 ± 0.0733 ** 0.0298 ± 0.0839 ** 0.0329 ± 0.1251 *

Thalamus 0.0529 ± 0.1061** 0.0257 ± 0.0798 ** -0.0332 ± 0.0946** -0.043 ± 0.1275**

Insular Cortex

0.0289 ± 0.0805** 0.0165 ± 0.0750 * -0.0094 ± 0.0806 -0.0398 ± 0.1196**

* α = 0.05 ** α = 0.01 +Baseline levels were obtained when participants were at rest in the scanner (no task engagement)


Table 5. Activation Contrast Results Between Glucose and Water Conditions

Results Between Glucose and Water Conditions

Magnitude (Glucose-Water)

Immediacy (Glucose-Water)

Outcome (Glucose-Water)

Anticipatory (Glucose-Water)

Nucleus Accumbens

-0.0088 ± 0.1795 0.0235 ± 0.1039 -0.0004 ± 0.1399 0.0086 ± 0.1414

Caudate 0.0087 ± 0.1534 0.0042 ± 0.1126 0.0080 ± 0.1148 0.0036 ± 0.1371

Putamen 0.0190 ± 0.1318 0.0098 ± 0.0926 0.0059 ± 0.0991 0.0143 ± 0.1468

Thalamus 0.0343 ± 0.1450 -0.0039 ± 0.1155 0.0189 ± 0.1136 -0.0048 ± 0.1488

Insular Cortex

0.0248 ± 0.1339 0.0080 ± 0.1049 0.0260 ± 0.1087 -0.0078± 0.1344

* α = 0.05 ** α = 0.01 Discussion

In this study, impulsivity and reward sensitivity were analyzed based on metabolic

manipulations with the implementation of the MID task. Reaction time data within a variety of

reward contexts was collected, as well as BOLD signaling during a reward anticipatory phase

and after reward outcome. The MID task design allowed for the reaction times of participants to

be collected in response to reward presentations that varied in their magnitude and immediacy.

The results of the reaction time analysis showed that participant reaction time was significantly

faster for more immediate rewards (p = 0.038), compared to delayed rewards. Between smaller

and larger rewards, larger rewards yielded faster participant reaction times (p = 0.022). This

finding is supported by other studies, as participants completing the MID task with reward

presentations based on indifference pairs also showed faster reaction times to immediate rewards

and those of larger magnitude (Luo et al., 2009). Indifference pairs in the Luo et al. (2009) study


were established by individualizing participant preferences for rewards, such that immediate and

delayed reward amounts were equally preferred (Luo et al., 2009). The faster reaction times in

this study also corresponded to changes in brain activity, with the anterior insula and putamen

showing greater response to trials in which participants had the opportunity to win more

immediate rewards (Luo et al., 2009).

In regards to the metabolic state manipulation, there was no significant effect (p = 0.166)

based on the drink condition of the participants (glucose, sucralose, or water). Adding on, there

was not a significant interaction between drink condition and reward magnitude (p = 0.552) nor

between drink condition and reward immediacy (p = 0.495). Prior studies analyzing the

interaction between metabolic state and sensitivity to reward magnitude and immediacy,

however, showed mixed findings. One study analyzed the fluctuations in human glucose levels

on future discounting rates with monetary rewards (Wang & Dvorak, 2010). Participants who

drank a glucose drink showed reduced rates of future reward discounting, while participants who

ingested a drink with artificial sweetener showed an increased rate in future discounting (Wang

& Dvorak, 2010). These differing results can potentially be attributed to the different tasks. The

MID task used in this study focused more on incentive-signaling during reward anticipation

periods, while in the Wang & Dvorak (2010) study, future discounting focused on reward-based

decision-making mechanisms. Participant impulsivity is more salient in this MID task design

(Luo et al., 2009), while self-control mechanisms can be implicated in decision-making designs,

like with the future discounting task. While the potential reaction time difference between sugar

conditions was a more secondary hypothesis, the overall hypothesis that glucose consumption

would result in attenuated reward sensitivity compared to the water condition was not supported


by the study results. Attenuated reward sensitivity would be associated with relatively slower

MID task participant reaction times, but there was no significant effect based on drink condition

supported with these results.

Whole brain analyses revealed significant activation differences for different reward

condition contrasts across all sugar conditions. In regards to reward magnitude, activation

contrasts (larger reward - smaller reward) showed significant BOLD activation changes in areas

of the left and right cerebral cortex. Reward immediacy contrasts (sooner rewards - later

rewards) showed significant BOLD activation in the right and left cerebral cortex, left caudate,

right putamen, and right pallidum. Beyond the reward anticipatory period, reward outcome (win

- loss) contrasts showed significantly differing BOLD activation in the left and right cerebral

cortex, left hippocampus, left accumbens, left putamen, and left pallidum. Along with these

subcortical brain region activations, both left and right cerebral white matter was identified as

showing BOLD activation changes, which may be due to technological and processing errors,

such as those associated with smoothing techniques and brain normalization in standard space.

At present, whole brain analyses separating drink condition have not yet been completed.

The region of interest analyses focused on the following brain regions: the nucleus

accumbens, caudate, putamen, thalamus, and insular cortex. The main effects results of the

activation contrasts across all sugar conditions revealed significant differences in regards to

reward magnitude, reward immediacy, and reward outcome conditions. For reward magnitude

and immediacy BOLD activation contrasts, there was a significant difference within the nucleus

accumbens, caudate, putamen, thalamus, and insular cortex. The significance was based on α =

0.01 for all brain regions, except for the nucleus accumbens for the reward magnitude contrasts


and the insular cortex for the reward immediacy contrasts, which were both significant at α =

0.05. Overall reward outcome BOLD activation contrasts revealed significant changes (α = 0.01)

within the nucleus accumbens, putamen, and thalamus. The overall anticipatory main effects

relative to baseline activation showed significant BOLD activation changes (α = 0.01) in the

nucleus accumbens, thalamus, insular cortex, and putamen (α = 0.05), but not within the caudate.

The magnitude of change in BOLD signals in these brain regions links incentive-driven behavior

to striatal and mesial forebrain structures, which is comparable to patterns of functional

activation found in prior studies (Knutson et al., 2000). Region of interest analyses separating

drink condition, focusing on glucose and water, were also performed. Activation contrasts

between glucose and water drink conditions (glucose - water) for reward immediacy, reward

magnitude, reward outcome, and the anticipatory period relative to baseline activation showed no

significant differences between the two drink conditions. These results suggest that the drink

condition had no effect and that sugar consumption did not alter BOLD activation patterns in

these specific denoted regions of interest. It is important to note, however, that these results are

still preliminary, as not all of the analyses have been completed.

Limitations of this study include the small sample size (N=32), as this limited the

statistical power of within-subjects effects. Moreover, the amount of time allotted between drink

ingestion and MID task implementation, along with fMRI imaging, may not have been adequate

for substantial metabolic change within participants to take place. In regards to imaging analysis,

preprocessing ICA denoising protocols may have resulted in both type I and type II errors,

although the reliability between manual experimenter denoising and automated FIX program

denoising was high, suggesting that these errors were at a minimum. Data collection may have


also been impacted by the study participants not following established protocols, such as fasting

prior to the scans.

Future studies can aim to explore the effects of various types of caloric sugars and

artificial, non-caloric sweeteners on impulsivity and reward signaling in the brain. Exploring the

interactions of sugars with other key nutrients in the body may reveal how different relative

amounts of macronutrients impact incentive-driven brain activity. Time-course changes involved

with the metabolization of macronutrients could also provide insight on how metabolic state

interacts with reward processing. It may also be interesting to examine if reward signaling differs

with sugar consumption in participants with different metabolic profiles, such as those with

metabolic syndrome or prediabetes compared to healthy individuals. In regards to replicating this

study, larger sample sizes to enhance statistical power should be implemented. Future

researchers may also decide to vary the time allotted between drink completion and task

engagement to improve absorption of sugars. Even with larger sample sizes and different

post-drink absorption times, there may still be no significant difference in MID task performance

between metabolic state manipulations.

The overall implications of the lack of effects observed in this study suggest that reward

sensitivity and incentive-driven behavior are not modeled very effectively with the MID task.

Prior studies conducted on animal models showed that metabolic state manipulations interacted

with incentive-signaling, as reward thresholds were altered within rats in intracranial

self-stimulation study models (Bruijnzeel et al., 2011). Although Knutson and colleagues (2000)

were able to connect striatal and mesial forebrain circuitry to incentive-signaling with the MID

task, the efficacy of the MID task may be limited in serving humans as an equivalent paradigm to


the intracranial self-stimulation model applied to animals. The more complex and intertwined

cognitive functioning mechanisms within humans can also impact attention processing as it

relates to reward-based incentive-signaling. It is also possible that the MID task is a viable

method for assessing incentive-signaling interactions with different human metabolic states;

there could simply be no effect of different sugars. Continued investigation of the limitations and

applicability of the MID task is needed to better orient future reward signaling research and to

create more sensitive reward threshold analogues that can be utilized in metabolic human studies.


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Running head: NEUROECONOMICS OF SUGAR€¦ · Running head: NEUROECONOMICS OF SUGAR 4 cognition and attention, as seen in hypoglycemia (Mergenthaler et al., 2014). Increased insulin