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ELECTROCORTICAL CHANGES OBSERVED IN THE N2 AS AN ELECTROCORTICAL CHANGES OBSERVED IN THE N2 AS AN

OUTCOME OF ATTENTION BIAS MODIFICATION TRAINING OUTCOME OF ATTENTION BIAS MODIFICATION TRAINING

Hayley Gilbertson hgilbert@nmu.edu

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ELECTROCORTICAL CHANGES OBSERVED IN THE N2 AS AN OUTCOME OF ATTENTION BIAS MODIFICATION TRAINING

By

Hayley Elizabeth Gilbertson

THESIS

Submitted to Northern Michigan University

In partial fulfillment of the requirements For the degree of

MASTER OF SCIENCE IN PSYCHOLOGICAL SCIENCE

Office of Graduate Education and Research

October 2020

SIGNATURE APPROVAL FORM

ELECTROCORTICAL CHANGES OBSERVED IN THE N2 AS AN OUTCOME OFATTENTION BIAS MODIFICATION TRAINING

This thesis by Hayley Gilbertson is recommended for approval by the student’s Thesis Committee and Department Head in the Department of Psychological Science and by the Dean of Graduate Education and Research.

__________________________________________________________Committee Chair: Dr. Joshua M. Carlson Date

__________________________________________________________First Reader: Dr. Jon Barch Date

__________________________________________________________Second Reader: Dr. Christina Hartline Date

__________________________________________________________Department Head: Dr. Adam Prus Date

__________________________________________________________Dr. Lisa Schade Eckert DateDean of Graduate Education and Research

08/05/2020

08/04/2020

08/03/2020

08/05/2020

01/07/2021

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ABSTRACT

ELECTROCORTICAL CHANGES OBSERVED IN THE N2 AS AN OUTCOME OF

ATTENTION BISA MODIFICATION TRAINING

By

Hayley Elizabeth Gilbertson

Anxiety disorders are currently one of the most predominant mental health conditions

worldwide. Increased anxiety is associated with elevated attentional focus to threat also known

as attentional bias to threat. Attention Bias Modification (ABM) is a type of computerized

training, attempting to reduce attentional focus for threatening stimuli and has been found to

successfully reduce symptoms of anxiety. Past studies have implemented ABM training as a

possible tool to modulate attention away from threat in attempt to decrease pathological anxiety.

The N2 is an event-related potential (ERP) detected in scalp EEG recordings that is associated

with conflict monitoring and complex cognitive functioning. Past studies have found that

anxious adults present increased N2’s during conflict processing, suggesting a relationship

between hyperactive conflict monitoring and anxious symptomology. This thesis measured the

effects of ABM on trait anxiety over a 6-week period. Each participant completed an attention

bias task, questionnaires for anxiety and emotion related traits, as well as an EEG session (i.e.,

flanker task) pre and post the 6-week ABM training period. ABM training was administered

through a modified dot-probe task; training attention away from threatening stimuli. The overall

purpose of this thesis was to establish the effect of ABM (compared to control) training on ERP

amplitudes and self-reported levels of anxiety. The results demonstrated a non-significant

correlation between pre-trait anxiety scores and pre- ΔN2. Additionally, there was no significant

effect of ABM on ΔN2 amplitudes or trait anxiety scores.

ii

Copyright by

Hayley Elizabeth Gilbertson

2020

iii

DEDICATION

I want to dedicate this thesis to my close friends and family who have supported me through long nights, lack of money, and busy days. I contribute a great amount of my success to these relationships/influences in my life. More specifically, I want to dedicate this thesis to my dad, Jeff Gilbertson, for shaping me into the individual I am today. Your selflessness and hard work is beyond inspiring. I would also like to thank my Mom, Vicki Vanderkolk for teaching me to always follow my dreams.

This thesis is also dedicated to all the individuals who have and/or are currently experiencing the negative effects associated with anxiety disorders. It is my hope that by identifying possible EEG-based neural biomarkers in the brain that it will increase the knowledge in order to establish efficacious treatment options and preventative measures in the future.

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ACKNOWLEDGEMENTS

The author firstly wants to thank her mentor and advisor, Dr. Joshua Carlson for all of the leadership, guidance, and instruction during this project and throughout her master’s program entirely. She also wants to thank the post-doctoral researcher of the CABIN lab, Dr. Lin Fang for all the assistance, management, and knowledge she provided her with throughout her master’s program. She also wants to thank her other two-committee members, Dr. Jon Barch and Dr. Christina Hartline for all of the great feedback and questions that influenced critical thinking into this project and related experiments. Additionally, she would like to thank the members of the CABIN lab for their assistance in data collection and recruitment of this project. Furthermore, the author would like to thank for the National Institute of Mental health for funding this project (as part of a larger research grant; R15MH1109051). Lastly, the author would like to thank Rourke Sylvain for his support and assistance throughout this endeavor. He has been an intellectual touchstone and has inspired her to delve deeper and question more. The format of this thesis follows the publication manual of the American Psychological Association (6th edition) and the Department of Psychological Science at Northern Michigan University.

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TABLE OF CONTENTS

ABSTRACT. ......................................................................................................................... (i)

COPYRIGHT. ....................................................................................................................... (ii)

DEDICATION. ...................................................................................................................... (iii)

ACKNOWLEDGEMENTS ................................................................................................... (iv)

TABLE OF CONTENTS ....................................................................................................... (v)

LIST OF FIGURES ............................................................................................................... (vI)

INTRODUCTION ..................................................................................................................... 1

LITERATURE REVIEW ........................................................................................................... 4

Anxiety .......................................................................................................................... 4

Attention to Threat ....................................................................................................... 5

Anxiety and Attention Bias to Threat .......................................................................... 7

Attention Bias Modification ....................................................................................... 10

ABM-Positive-Search Training .................................................................................. 12

Error-Related Negativity (ERN) ................................................................................ 13

N2 ................................................................................................................................. 15

METHOD ................................................................................................................................ 17

Participants .................................................................................................................. 17

Procedure ..................................................................................................................... 17

Self-Reported Measures ............................................................................................. 18

State-Trait Anxiety Inventory .................................................................................... 18

Dot-Probe Task ........................................................................................................... 19

Flanker Task ............................................................................................................... 20

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Attention Bias Modification Training .........................................................................21 EEG Recording .......................................................................................................... 24

Data Processing ......................................................................................................... 24

Analytic Plan............................................................................................................... 25

RESULTS ................................................................................................................................ 26

N2 Manipulation Check ............................................................................................. 27

DISCUSSION .......................................................................................................................... 29

Effect of ABM Training on Self-Reported Anxiety and ΔN2 .................................... 32

Clinical Implications....................................................................................................34

Limitations and Future Directions ............................................................................. 35

Conclusions ..................................................................................................................37

REFERENCES ........................................................................................................................ 39

APPENDIX A ........................................................................................................................... 59

Figures .........................................................................................................................59

APPENDIX B……………………………………………………………………………………

IRB approval letter ...................................................................................................... 37

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LIST OF FIGURES

Figure 1. Example of the modified flanker task paradigm ........................................................ 59

Figure 2. 10-20 Electrode map highlighting FCz (electrode 4) ................................................. 59

Figure 3. Example of the dot-probe task during ABM training displaying a threatening (left) and neutral face (right) ............................................................................................................................... 59

Figure 4. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the control group (PRE data) . .................................................................................. 60

Figure 5. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the control group (POST data) .................................................................................. 60

Figure 6. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the experimental group (PRE data; i.e., pre 6-week ABM training) . ......................... 61

Figure 7. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the experimental group (POST data; i.e., post 6-week ABM training) . ..................... 61

Figure 8. Correlation between pre-training self-reported STAI – T score and pre-training ΔN2 amplitude (in μV) across all participants ......................................................................................................... 62

Figure 9. Correlation between pre-training self-reported STAI – T score and pre-training incompatible ΔN2 amplitude (in μV) across all participants ..................................................................................... 62

Figure 10. Evidence of the correlation (or lack thereof) between pre-training STAI-T score and pre-training ΔN2 amplitudes (in μV) in the experimental group...................................................... 63

Figure 11. Although not significant, this chart provides evidence that ABM may correlate with the ΔN2, suggesting it may be related to conflict monitoring. .................................................................. 63

1

Introduction

Anxiety is associated with increased attentional bias to threat and decreased executive

functioning, decreased cognitive control, and excessive error monitoring (Mogg & Bradley,

2016). This increases an individual’s susceptibility to external distractions, suggesting a

relationship between emotion and cognition in pathological anxiety.

Anxiety is characterized by both the irrational perception of threat and the duration and

intensity of fear experienced. Over 25 percent of individuals will be diagnosed with an anxiety

disorder during their lifetime (Kessler, Berglund, Demler Jin, Merikangas, & Walters, 2005).

The mapping and identification of neuro-correlates associated with anxious symptomatology

would allow for possible improved treatments and the establishment of preventative measures.

Current popular treatments for anxiety disorders consist of Cognitive Behavioral Therapy

(CBT) and Pharmacotherapy; both have been found to have a 50% success rate, while

pharmacotherapy can be generalized and susceptible to negative side effects such as drug

dependence (Bar-Haim, 2010; Hofmann, Asnaani, Vonk, Sawyer, & Fang, 2012). This can lead

to an increase in anxious symptoms and a prolonged recovery (Bishop, 2009). Attention bias

(AB) is the preferential tendency to attend to threat-related stimuli, in comparison to a neutral or

positive stimulus, and is a cardinal symptom of anxiety. Some research suggests an increased AB

to threat is a causal factor in the development and maintenance of anxiety (Beck & Clark, 1997;

Dennis & Chen, 2006; MacLeod, 2002). Researchers have shown a significant link between

anxiety disorders and hyperactive attention bias to threatening stimuli (Mogg & Bradley, 2016).

Recently, established Attention Bias Modification ABM treatments have ascertained the

ability to transfer participants' automatic attentional processing away from threat, towards non-

threatening stimuli, resulting in an improvement of the cognitive mechanisms associated with

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inhibition and down regulation of attentional bias to threat (MacLeod et al, 2002; Mogg &

Bradley, 2006; Nelson et al, 2015).

Previous studies have examined the effectiveness of ABM using neuroimaging measures

such as MRI/fMRI (e.g., Aday & Carlson, 2017). This approach is costly and can be intrusive.

Electroencephalography (EEG), which measures electrocortical activity, and in particular interest

to this thesis, event-related potentials (ERP) could provide a more cost effective and relatively

non-invasive option, compared to fMRI. Two ERP’s previously associated with error processing

and conflict monitoring are the Error-Related Negativity (ERN) and the N2. The ERN is

associated with anxiety due to its role in worry, apprehension, and cognitive inhibition (Moser,

Moran, Schroder, Donnellan, & Yeung, 2013). The N2 is associated with conflict processing and

the demand for increased cognitive control (Dennis & Chen, 2009). Cognitive control and

inhibition are associated with the modulation of top-down and bottom-up processing. Top down

processing is goal-directed, involving effortful processing of one’s control of a task. Bottom-up

processing is stimulus driven involving unconscious automatic processing (Mogg & Bradley,

2016). Mogg and Bradley discovered that anxiety enhances stimulus-driven attention whereas

top-down cognitive mechanisms associated with inhibition become less effective (Mogg &

Bradley, 2016). The extent to which ABM modifies conflict monitoring (N2) processes is

unknown.

The purpose of this project is to identify and improve the understanding of electro-

cortical changes following ABM. Specifically, this thesis seeks to assess the extent to which

ABM reduces N2 ERP amplitudes, and self-reported anxiety.

3

Overall Research Question:

● Will ABM lead to a decrease in electrocortical measures of conflict processing in high-

anxious individuals measured through N2 amplitude?

Hypothesis 1: heightened levels of trait anxiety are negatively correlated with ΔN2 (ΔN2

= incongruent flanker N2 - congruent flanker N2) amplitudes. Such that, individuals with

heightened self-reported trait anxiety scores present larger negative ΔN2 amplitudes.

Hypothesis 2: 6-weeks of ABM training decreases ΔN2 amplitude and self-reported

anxiety levels.

4

Literature Review

Anxiety

Anxiety is associated with reduced emotional conflict adaptation (Larson, Clawson,

Clayson & Baldwin, 2013), impaired cognitive control (Sehlmeyer, Konrad, Zwitserlood, Arolt,

Falkenstein, & Beste, 2010), increased attention to threatening stimuli (Dennis & Chen, 2009),

avoidance behavior (Larson, Clawson, Clayson & Baldwin, 2013), and hyperactivity in specific

neural structures associated with emotional regulation and conflict processing (Mogg & Bradley,

2016). High-trait anxious individuals have an increased attention to threat-related stimuli and

diminished ability to transfer attention away from threat. This suggests a decrease in cognitive

control and difficulty orienting attention to goal-relevant stimuli (Eastwood, Smilek, & Merikle,

2003; Fox, Russo, Bowles, & Dutton, 2001; Mogg, Mathews, & Eysenck, 1992).

Research investigating emotional conflict processing has found that individuals

diagnosed with Generalized Anxiety Disorder (GAD) have an inability to implicitly regulate

emotional conflict (Etkin & Schatzberg, 2011). Furthermore, they found deficits in the neural

regions associated with implicit regulation during emotional conflict processing. That is, GAD

patients fail to show the typical increase in ventral cingulate activity and associated dampening

of amygdala activity during implicit emotion regulation (Etkin & Schatzberg, 2011). This

suggests a decrease in inhibitory control mechanisms and a deficit in attentional regulation.

The two most prevalent treatments for anxiety disorders include cognitive-behavioral

therapy (CBT) and pharmacotherapy; resultant in a 50% success rate for each approach in

clinical settings (Bar-Haim, 2010; Hofmann, Asnaani, Vonk, Sawyer, & Fang, 2012). The

importance of improving current treatments and the continuation of establishing innovative and

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more successful options is imperative in psychology (and related fields). Moreover, developing

less invasive and more effective anxiety treatments for children is also of paramount importance

(Bar-Haim, 2010).

Trait anxiety is the increased tendency to be affected by environmental stressors (Fales et

al, 2008) while anxiety sensitivity represents the increased susceptibility to experience anxious

symptomology and the effects it has on behavior (McNally, 2002). Individuals with anxiety have

neurocognitive deficits in cognitive control and inhibition. Trait anxiety and anxiety sensitivity

have been linked to response inhibition in individuals who have anxiety. The concepts of anxiety

that trait anxiety and anxiety sensitivity represent are still unknown (Lilienfeld, 1996); although

it is assumed that the two work together, while trait anxiety is responsible for cognitive factors,

anxiety sensitivity is assumed to represent one’s proneness/probability to experience anxiety.

Recent research has focused on the relationship between cognitive differences, anxiety traits, and

electrophysiology for more insight into the psychopathology of psychiatric disorders related to

anxiety (Karch et al., 2008; Manly, Robertson, Galloway, & Hawkins, 1999; Roche, Garavan,

Foxe, & O’Mara, 2005).

Attention to Threat

Past research has provided evidence that AB to threat is increased in a subset of

individuals with anxiety disorders, linking this bias to the development and maintenance of such

disorders (Dennis & Chen, 2009). This bias can be measured through conflict interference of

cognitive control processing of attention (Fenske & Eastwood, 2003; Simpson et al., 2000;

Williams, Mathews, & MacLeod, 1996). When processing information, highly salient emotional

cues are prioritized over stimuli that are less salient. This process is crucial for survival, eliciting

avoidance of threat or signaling approach of a positive outcome. When an individual comes

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across emotional stimuli, their perceptual and attentional systems prioritize the stimuli associated

with emotional mechanisms, ensuring that the individual is aware of this information primarily

(Sussman et al., 2016). Evidence of this was displayed in an experiment conducted by Ohman

and colleagues, measuring reaction time on stimulus detection, comparing threat-related words

and neutral/positive words. Results displayed that the participants responded more rapidly to the

words associated with threat compared to the neutral and positive words (Ohman et al., 2001).

Another example includes an experiment using a set of rapidly displayed images of threatening

and non-threatening stimuli, resulting in the participant’s ability to identify more of the

threatening images post presentation in comparison to neutral stimuli (Anderson, 2005).

Attention mechanisms allow individuals to selectively attend and respond to specific

stimuli (Bishop, 2009). Selective attention is the process of orienting attention towards one thing

while simultaneously disengaging focus towards all other external stimuli. This is regulated by

the interplay between “bottom-up” and “top-down” processing (Bishop, 2008). Top down

processing refers to goal-directed behaviors or processes, increasing one’s control of a task.

Bottom-up processing is stimulus driven, referring to unconscious automatic processing (Mogg

& Bradley, 2016). The resources for cognitive processing associated with attention and

emotional processes compete when increased threat is perceived. Top-down control is

diminished and attention towards emotionally salient stimuli is increased. (Easterbrook, 1959;

Hanoch & Vitouch, 2004; Leith & Baumeister, 1996; Meinhardt & Pekrun, 2003; Wood,

Mathews, & Dalgleish, 2001). Therefore, one’s AB is altered by the reciprocal influence

between top-down and bottom-up processing. In other words, an individual’s attention is

constantly queued in the environment through perceived novel/salient stimuli while

simultaneously competing for more focused and goal-driven cognitive ability (Bishop, 2008).

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AB reflects the conflict interference on cognitive control processing of attention (Fenske

& Eastwood, 2003; Simpson et al., 2000; Williams et al., 1996). AB to threat refers to the

unconscious tendency to attend towards threatening stimuli in comparison to neutral/positive

stimuli. This is thought to be overrepresented in anxious populations, linking them to an

increased bias to threat. Individuals with low self-reported anxiety display low AB to threat and a

higher tolerance to threat-related situations (Bishop, 2008).

There are many examples of this dichotomy between anxious and non-anxious

individuals. Williams et al. (1996) investigated the effects of AB on task performance using an

emotional word Stroop task. Participants were presented one word at a time on a computer

screen. The words were split into two groups: emotionally salient and neutral and were displayed

in various colors. Participants were asked to respond to the color of the ink while ignoring the

word's implication. The participants that were classified as anxious had a slower response time

when responding to threat salient words (Williams et al., 1996). Another study, using a dot-probe

task, measured AB in anxious and non-anxious controls. The study found the anxious

participants were faster to detect the probe when behind threat-related trials compared to neutral

trials (Mogg & Bradley, 1998). These findings suggest that anxious individuals tend to exhibit a

heightened AB to threatening stimuli. Due to the emotional and cognitive factors linked to

anxiety, it makes sense that there is a relationship between anxiety and diminished performance

in situations encountering emotional or cognitive conflict (Mogg & Bradley, 2016).

Anxiety and Attention Bias to Threat

Individuals with anxious symptomology have enhanced attentional disruptions to threat-

related stimuli (Eastwood et al., 2003; Fox et al., 2001; Mogg et al., 1992). As mentioned

previously, AB to threat is seen in most individuals (regardless of anxious affiliation), although

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the modulation of attention towards threatening stimuli is more persistent and impairing in

individuals diagnosed with an anxiety disorder (Mogg & Bradley, 2016). Individuals

experiencing anxiety often superimpose negative salience onto non-threatening stimuli

(Pergamin-High et al., 2015). Evidence of selective processing of threat in anxiety disorders

suggests a critical relationship between etiology and ailment of such disorders (Bar-Haim et al.,

2007).

Avoidance of threat is another potential factor for decreased task performance in anxious

populations. The degree to which anxious individuals efficiently recruit cognitive resources can

be completely inhibited when fear is present (Bishop et al., 2004; Compton, 2003; Compton et

al., 2007). Bishop and colleagues (2004) conducted a study using threat-related distractors (i.e.,

fearful faces) during a non-emotional decision making task in individuals experiencing anxiety.

Although task performance was unaffected, decreased neural activity was found in regions

linked to cognitive control, such as the left prefrontal cortex and dorsal Anterior Cingulate

Cortex (dACC; Bishop, et al., 2004). Researchers suggest this may reflect a neural mechanism

attempting to mitigate the degradation of task-performance when bottom-up emotional and

conflict mechanisms are enhanced (Gray, 2004).

The dot-probe task (sometimes also referred to as the visual-probe task) intrinsically

measures selective attention to threat in order to assess one’s AB (MacLeod, 1987,1988;

Thigpenet al., 2018). The typical experiment starts with a fixation point (center of the screen),

followed by the concurrent display of threatening and non-threatening cues on the left and right

side of the screen. After these stimuli disappear, a probe (i.e., dot, letter, arrow, etc.) replaces one

of the previously displayed cues (threatening vs non-threatening). The probes are administered

randomly and with equal probability between cues. Participants are asked to respond to the

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location of the dot as quickly and as accurately as possible. AB is measured through reaction

time to each probe. AB to threat is reflected through a decreased response time to threat-

congruent trials, i.e. trials in which the probe replaces the threatening cue. An increased response

time in threat-incongruent compared to threat-congruent trials indicates that the participant’s

attention is unconsciously biased towards the side of the screen displaying the threatening

stimuli. This is often referred to as vigilance (Mogg & Bradley, 2016). Conversely, the

behavioral trait displayed when individuals react quicker to threat-incongruent trials compared to

threat-congruent trials is classified as avoidance (Mogg & Bradley, 2016). AB scores are

determined by the RT difference of threat-incongruent trials and threat-congruent trials (i.e.,

threat-incongruent trials minus threat-congruent trials). Positive scores represent vigilance while

negative scores reflect avoidance. An individual is said to have no attentional bias, if their

incongruent – congruent score is near zero.

Mounting evidence implicates the prefrontal cortex (PFC) and the extended amygdala

network as regions associated with AB mechanisms and targets of neuroplastic change following

ABM training (Aday & Carlson, 2017). Human lesion studies have demonstrated that the

amygdala is critical for the allocation of attentional resources to threatening stimuli. For

example, research involving an attentional blink paradigm showed that healthy observers

robustly perceived verbal stimuli of aversive content compared with stimuli of neutral content.

Conversely, patients with bilateral amygdala lesions showed no enhanced perception for such

aversive stimulus events (Anderson and Phelps, 2001). Furthermore, Vuileumier and colleagues

(2004) conducted event-related fMRI investigations assessing the amygdala’s influence on the

visual cortex when healthy, hippocampus lesion, and amygdala lesion patients were presented

emotional visual stimuli (i.e. fearful faces). The results demonstrated that, in contrast to the

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patients with amygdala lesions, both healthy patients and patients with hippocampal lesions had

enhanced activation in the fusiform and occipital cortex for threatening compared to neutral

stimuli (Vuilleumier et al., 2004). Moreover, the nucleus basalis of Meynert (nbM), which

contain acetylcholine (Ach) neurons, are a major projection site of the amygdala and the nbM

sends projections diffusely throughout the cortex—including visual and other sensory cortices—

to facilitate perceptual processing (Himmelheber et al., 2000; Himmelheber et al., 2001; Peck &

Salzman, 2014; Sarter et al, 2003;).

In addition to the amygdala, numerous sub-regions of the PFC play an integral role in

attention orientation and modulation. For example, Wolf et al. used eye-tracking techniques to

investigate the effect the ventromedial PFC (vmPFC) has in orienting attention to fearful stimuli

(2014). The results concluded that patients with vmPFC lesions oriented their attention to eye

regions of fearful faces significantly less than those without vmPFC lesions. Prior human and

non-human primate investigations have shown that the vmPFC and the amygdala are densely and

reciprocally connected (Barbas, 2000; Ghashghaei & Barbas, 2002; Roy et al., 2009).

Furthermore, the attention orientation reflected by amygdala activity has been shown to be

correlated with activity in the ventral and dACC (Carlson et al., 2009). This amygdala-PFC

network may reflect the neural mechanism for allocating visual attention to emotionally and

socially salient stimuli.

Attention Bias Modification

As mentioned previously, anxious individuals have a heightened AB towards threatening

stimuli. This schema towards threatening stimuli has been found to play a large role in the

etiology and maintenance of such disorders (Beck & Clark, 1997; Eysenck, 1997; Rapee &

Heimberg, 1997; Williams et al., 1997).

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Using neuroimaging and cognitive/behavioral methodologies, researchers are able to peer

into the neural mechanisms associated with such mental health disparities, establishing a

biological foundation, on which more efficacious treatments can be developed. Examples of this

include Gabrieli’s research demonstrating improved reading performance amongst dyslexic

children by administering an auditory phonological processing schema of print words (2009). In

addition, Fisher et al. found schizophrenia patients who underwent auditory training displayed

marked improvements in cognition, verbal working memory, and verbal learning memory as

well (2009). These examples display cognitive-behavioral treatments that improved psychiatric

disorders.

In light of the cognitive-behavioral therapies addressed above, recent investigations have

employed a modified dot-probe task, used to measure AB, in attempts to modulate a participant’s

AB towards or away from task relevant stimuli and has been classified as ABM (Mogg &

Bradley, 2016). ABM was first implemented in two studies, which evaluated the effect of a dot-

probe task on anxiety scores. The investigations demonstrated that participants who underwent

more than 6000 training trials, where the probe location consistently appeared behind the neutral

stimuli (opposed to threat), displayed not only lower AB scores but also a significant decrease in

trait-anxiety scores (reviewed in Mathews and MacLeod, 2002). These studies initiated the rising

interest in ABM research and its effects on anxiety symptoms (Bar-Haim, 2010).

Current investigations also attempt to understand the neural mechanisms of ABM

(Browning et al., 2010; Mansson et al., 2013; Mansson et al., 2016; Maslowsky et al., 2010;

Taylor et al., 2013). Similar to the regions discussed above (see AB section), the amygdala and

prefrontal cortices have been shown to reflect a scalar and reciprocal influence on the attentional

mechanisms, respectively (Away & Carlson, 2017; Taylor et al., 2013). Taylor et al.

12

administered an attention modification program (AMP) to participants with high social anxiety,

and provided evidence of attenuated activation from pre- to post-AMP in the bilateral amygdala,

bilateral insula and subgenual anterior cingulate cortex (2013). Furthermore, participants

displayed enhanced activation in prefrontal cortices, such as the left anterior dorsal ACC, left

dorsolateral PFC, vmPFC and right ventrolateral PFC, following the AMP (Taylor et al., 2013).

This evidence suggests that decreased activation in the ACC and amygdala, as well as enhanced

activity in the PFC, following AMP may reflect overall improvements in the functioning of

attentional neural mechanisms during emotional processing. These findings add to the mounting

evidence implicating the ACC and the medial PFC as neural regions strongly associated with

emotional processing and ABM (Etkin et al., 2011).

ABM-Positive-Search Training

Although there is ample evidence regarding an anxious individual’s susceptibility

towards threat, anxiety disorders have been diagnosed without any perception to threat (Van

Bockstaele et al., 2014). ABM-positive-search training focuses on transferring one’s automatic

attention towards positive environmental cues. In this method, participants are instructed to

respond to positive stimuli only, inhibiting attention towards threat. This method could

potentially be administered to patients with anxiety disorders whose symptoms vary from those

associated with vigilance to threat (Dandeneau et al. 2007; De Voogd et al., 2014; Waters et al.,

2013).

Researchers discovered that using the same tools that measure AB could also be

applicable to manipulate one’s attention away from threat. As mentioned previously (Anxiety and

AB to threat), the dot-probe task is used to measure attentional bias to threat. The task is also

used to implement ABM training. The use of the dot-probe task in attention training involves an

13

increase in target probes associated with neutral/positive stimuli in attempt to guide the

participant’s attention away from the threatening stimuli. Numerous (>100) trials are required to

alter one’s pattern of attention, shaping an altered perception to threat. In one study, participants

who displayed high-trait anxiety participated in an attention training dot-probe task (consisting of

6000-7500 trials each), and displayed a significant decrease in trait-anxiety when pre and post

training were compared. In another study, patients diagnosed with General Anxiety Disorder

(GAD) completed dot-probe training comprised of 160 trials (8 sessions in four weeks). This

resulted in 58% of GAD patients no longer meeting the DSM-IV requirements for the disorder

(Bar-Haim, 2010).

Error-Related Negativity (ERN)

An acquisition process of EEG measurements, where an isolated electro-cortical

waveform is segmented into epochs, temporally bound to an event, and averaged across trials—

is used for analysis, and has become a popular technique deployed for cognitive and behavioral

investigations. These waveforms are known as event-related potentials (ERPs), first recorded in

1935 by Hallowell Davis et al. (1939), multiple facets of psychological and neurological research

deploy this technique in order to better understand the temporal dimensions of electro-cortical

activity emitted by the brain. In particular, research investigating performance monitoring and

error detection have provided substantial evidence of a specific ERP associated with these

behaviors classified as the error-related negativity (ERN; Riesel et al., 2013; Yeung et al., 2004).

The ERN is an ERP closely associated with anxiety due to its nature in apprehension/worry,

concept control, and cognitive inhibition. It is localized to the frontocentral electrode cites,

displayed approximately 50-100 ms after an erroneous response has been conducted (Zambrano-

Vazquez & Allen, 2014). ERN amplitude can vary across individuals depending on participants'

14

perceived response accuracy; however, latency is predominantly consistent (Bress et al., 2015).

Research suggests a strong correlation between the ERN and increased activity in the Anterior

Cingulate Cortex (ACC) due to its association with conflict, action monitoring, emotional

influences, and motivational factors (Dennis & Chen, 2009; Hajack et al., 2010). Accompanied

physiological changes include increased skin conductance, heart rate activity, and the startle

reflex (Bress et al., 2015).

In addition, as discussed above, research suggests that the ACC is involved in attention

bias to threat and is modulated by ABM training. With research providing evidence of ACC

modulation due to ABM training (Taylor et al. 2013), ABM may modulate additional functions

of the ACC, such as error monitoring as measured by the ERN. As mentioned previously,

disruptions in attentional processing to threat-related stimuli reflect the interference between

emotional and cognitive processing. Studies focusing on generalized anxiety disorder (GAD)

suggest that an individual’s inability to modulate attention and inhibit cognitive processes are

due to decreased ability to detect and respond to conflict (Etkin et al., 2010). This interference is

heightened in anxious compared to non-anxious individuals, due to their increased susceptibility

of emotional processing (Easterbrook, 1959; Hanoch & Vitouch, 2004; Leith & Baumeister,

1996; Meinhardt & Pekrun, 2003; Wood et al., 2001).

The ERN has been found to be noticeably enhanced in individuals with GAD and OCD

(Endrass et al., 2008; Gehring et al., 2000; Hajcak et al., 2008; Weinberg et al., 2010; Weinberg

et al., 2012). Family history of mental health (Carrasco et al., 2013; Riesel et al., 2011) and

inability to inhibit behavior at a young age (McDermott et al., 2009) are also linked to a

heightened ERN; both classified as common risk factors for anxiety. In two studies conducted by

Nelson, et al. (2015; Nelson, Jackson, Amir, & Hajcak, 2017), researchers examined the effect of

15

ABM on the neural processing of errors (i.e., ERN). Participants were administered single

session ABM training through a modified version of the spatial cueing task (Posner, 1980). Error

processing was measured through ERN amplitude, elicited through a flanker task. Participants

completed the experiment conversely (i.e. ABM before ERN was measured vs. ABM after ERN

was measured). The ERN was significantly decreased (i.e., less negative) in participants who

completed ABM prior to eliciting the ERN, suggesting that a single session of ABM

disengagement towards threatening stimuli may influence ERN amplitude through decreased

neural activity associated with anxiety (Nelson, et al., 2015). These findings suggest that ABM

may be successful in modulating attention away from threatening stimuli, signaling the ERN as a

possible mechanism of ABM-related changes.

N2

The N2 is stimulus-locked, scalp-recorded ERP displayed when individuals require

increased cognitive processing (Dennis & Chen, 2009). It is associated with the frontal midline

regions, displaying a negative amplitude occurring 200-350 ms after a stimulus was presented.

The mean amplitude is configured through the comparison of incongruent and congruent N2

amplitudes. The amplitude of incongruent trials are increased, compared to congruent trials;

when the task exhibits conflict (i.e. incongruent flanker display; < < > < <) or when the

incongruent stimuli is highly salient (i.e. it is associated with inhibitory control mechanisms;

Larson & Clayson, 2011). The N2 indicates the need for increased cognitive control, improving

one’s response to future conflict and increased inhibition to unrelated stimuli. The proposed

brain structure associated with N2 is the ACC responsible for conflict and action monitoring.

The amygdala and other limbic regions relay information to the ACC, further associating with

motivational and emotional factors. The ACC holds an important role in the ERN and N2, due to

16

its neural basis integrating emotion and cognition. Conflict-monitoring theory suggests that N2

amplitudes (mean amplitude from 300-350 ms) are larger when task-irrelevant stimuli is

presented, suggesting one must exhaust more attention when presented unrelated stimuli (Dennis

& Chen, 2009).

Taken together, research suggests that individuals with high levels of trait-anxiety have

an increased AB to threat. Evidence of selective processing of threat in such individuals proposes

there is a relationship between etiology and maintenance of anxiety disorders. Due to the high

concentration of individuals experiencing anxiety and the poor success rate of current treatments

(i.e., 50% success in CBT and pharmacotherapy), it is vital that new treatment options are

established for use in clinical settings. Top-down and bottom-up processing together determine

one’s selective attention towards external stimuli. Researchers have developed ABM in order to

transfer selective attention towards more goal-oriented behaviors, indirectly decreasing anxiety

through increased cognitive performance/decreased stimulus driven processing. The N2 is an

ERP associated with conflict processing and has shown to be elevated in anxiety. A decreased

N2 amplitude displayed in individuals suggests that ABM could influence cognitive processing,

possibly manipulating psychological/physiological components associated with trait anxiety.

Therefore, a decreased ΔN2 was expected following 6-week ABM training

17

Methods

Participants

Eighty-four men and women (M = 31) between the ages of 18-38 (M = 21.83, SD = 4.82)

were recruited from the Marquette Community. Participants were chosen randomly to be part of

the control (N = 32; i.e., completing six weeks of training, not implementing ABM training

towards or away from threat) or experimental group (N = 52 ; i.e., completing six weeks of

ABM-training away from threatening stimuli). Fifty-six of those recruited were not part of the

final results due to artifact rejection (N = 5), training attrition (N = 18), or quarantine in response

to the COVID19 pandemic (N = 33). The final analysis included 28 participants, with 14 in the

experimental group, and 14 in the control group. Participants were informed of the study through

various posters in the community and numerous Facebook posts from the Cognitive Affective

Behavioral and Integrative Neuroscience (CABIN) lab page. Data for this experiment were

collected as part of a larger research project (NIMH #R15MH1109051), following the same

recruitment method.

Procedure

Participants completed a pre-screening survey verifying eligible age range (18-42),

possession of a smartphone, normal (or corrected to) vision, current psychological treatment, any

past/current diagnosis of mental illness, history of head injury and lost consciousness, current

medications, anxiety elicited through small spaces, status of childbearing, and possible metal

placed inside or outside the body that could not be removed. Possible exclusion criteria included

recent head injuries (e. g., concussion), history of neurological disorders, current consumption of

psychoactive medication, and/or currently seeking mental health treatment through any form of

18

counseling or therapy. If participants met criteria based on the pre-screening survey, they were

contacted by a research assistant in the CABIN lab through the google voice text application.

Participants then completed one task and three self-report surveys to verify their

eligibility in the study: the dot probe task, the State and Trait Anxiety Inventory (STAI), the

Depression and Stress Scale (DASS), and the Cognitive Emotion Regulation Questionnaire

(CERQ). Participants were required to obtain an AB incongruent - congruent score of at least 7

ms and STAI-T score of 40 or higher to be included for participation in the study. If individuals

met inclusion criteria, they were asked to provide general background information such as age,

gender, dominant hand, and ethnicity. Next, they completed the screening (which measured

behavioral effects and AB score based on a dot-probe task) and the three previously mentioned

self-report surveys. If participants did not meet requirements of the in-person screening, they

were compensated $10 for their participation in the screening. If participants did meet inclusion

requirements for the study, they were compensated $65 following their post EEG session.

Self-Report Measures

State-Trait Anxiety Inventory.

The STAI was created to measure the prevalence of anxiety in individuals. This measure

has two subscales: State and Trait (Leal et al., 2017). State anxiety is prompted through

environmental factors; therefore, on the state scale participants were asked to respond how they

felt in that moment. Trait anxiety involves a character predisposition that increases susceptibility

to anxiety and is evaluated through factors that suggest an increase prone to anxiety (e.g, general

states of calmness, confidence, and security; (Guillen-Riquelme & Buela-Casal, 2011; Julian,

2011). Each category of anxiety involves 20 questions, assessed on a 4-point Likert-scale (1 =

almost never, 4 = almost always). Each subset score (state and trait anxiety) has a range of 20-80.

19

Total scores for each are determined by the sum of anxiety symptoms, simultaneously

subtracting responses indicating the absence of symptoms. A score of 39 or higher indicates

clinically significant systems for state anxiety (Julian, 2011). Participants were selected for high

levels of anxiety (≥ 40 on the STAI-T) and heightened attentional bias to threat.

Dot-probe task.

A dot-probe task requires an incongruent-congruent difference score of greater than or

equal to seven ms. It measures intrinsically selective attention in two stimuli (See figure 3;

Thigpen et al., 2018). It is important for assessing and altering an individual’s AB. Participants

start with a fixation cue displayed in the center of the screen (1000ms). Then, there were two

stimuli displayed on either side of the screen (100ms). Following stimulus display, a dot was

displayed on either the same or the different side than the stimulus was displayed on and

remained until a response was made. Participants were asked to respond to the orientation of the

dot as quickly as possible, using their right hand; right index finger on the first button to respond

to dots displayed on the left side of the screen, right middle finger on the second button in

response to right-sided targets. AB was measured through response time of each trial; suggesting

a faster response indicates the participants’ unconscious attention was already biased towards

that side of the screen, with slower responses indicating fixation on the opposite side. Faster

reaction times to congruent threat stimuli predict a bias toward related stimuli (MacLeod, 1987-

1988; Mogg & Bradley, 2016).

Prior to beginning participation in the study, individuals were required to read and sign

the informed consent form, confirming they understood the procedure and agreed to volunteer in

data collection. Participants were notified in the consent form that they were volunteers and

allowed to leave at any time during the study. Once deemed eligible to participate, individuals

20

were asked to provide additional demographic information; full first, middle, and last name, date

of birth, and city/state of birth. The participants were measured for head circumference in cm in

order to anticipate the corresponding EEG cap size. In addition, the participants were scheduled

for their Pre-EEG session and the CABIN ABM application was installed on their smartphones.

Participants were then randomly placed in a control (completing ABM training sessions through

the modified version of the dot-probe task that are not modulating attention towards or away

from threatening stimuli) or an experimental group (using a modified version of the dot-probe

task to alter individuals attention away from threat).

During the EEG, participants were required to complete a seven block computer

administered flanker task (see Flanker task - above) and 8 minute resting state measure where

they were instructed to remain as relaxed as possible.

Flanker task.

A Flanker Task is the most common and effective measure used to elicit an ERN (Yeung,

Botvinivk, & Cohen, 2004). Participants were administered a modified flanker task using the

EPrime 3.0 presentation software. Participants were seated 59 cm from the computer screen.

Responses were recorded with a chronos box placed in front of the computer monitor. For each

trial (see Figure 1), participants were presented with a fixation point (i. e., a white plus sign in

the middle of the screen) for 1000ms. Following the fixation cue, the screen displayed five white

arrows centered on a black screen for 200 ms. Each trial could be classified as a congruent trial

(e.g., < < < < < or > > > > >) or an incongruent trial (e.g., < < > < < or > > < > >). All trial types

were administered with equal probability. Each trial had an inter-trial interval of 1000 to 1500

seconds where the participants were instructed to provide the exhibited direction of the center

arrow (left or right) using the corresponding buttons on the chronos box. The task included one

21

practice block (20 trials) and seven training blocks, consisting of 60 trials each, completing a

total of 420 trials. This experiment complied with a predefined protocol, which instructed

procedures for the collection of demographic information, fitting of the EEG cap, experiment

progression and recording, and subsequent cap cleaning (See Flanker protocol in APPENDIX).

Attention bias modification training.

The ABM task used was a slightly altered version of the ABM training dot-probe task

conducted by Aday and Carlson (2017). Participants completed training via their personal

smartphone on the “CABIN” cell phone application. To maintain consistency with the previous

paradigm, duration of the training remained six weeks and the same set of facial stimuli was

utilized as the original task. Sixty word stimuli, taken from the Affective Norms for English

words (ANEW), were added to the ABM task; they were rated by valence and arousal,

categorizing them as neutral or fearful stimuli. Neutral and fearful word pairs were matched by

length and frequency, for a total of 30 pairs (Bradley & Lang, 1999; Stevenson, Mikels, &

James, 2007).

The application was compatible with apple and android devices. Prior to beginning

training, participants were required to read through the training instructions and requirements.

They were then asked if they had any questions regarding training, the cell phone application, or

anything else related to the experiment. Training consisted of six weeks, with a requirement of

no more/no less than six training sessions to be completed per week. This resulted in a total of 36

sessions. To better control regulation of training, completion of one training session a day was

preferred for participants; performance of up to three sessions per day was accepted. Participants

were issued daily reminders to complete their sessions and warning messages if further action

was needed (e.g. participant missed a session). Participants were excluded from the experiment if

22

they were behind on their sessions for more than one week (as stated in the informed consent

document) and provided with $10 of compensation. The procedure of the ABM training is

explained in detail below:

Once the participant opens the “CABIN” application, a “Prepare for Trial” screen was

displayed; requiring the individual to set their phone to “Do Not Disturb” and ensure their level

of brightness is set to its highest setting. This screen also reminds participants to find a place

where they can complete the training distraction-free. Once the participants were prepared to

begin training, they were administered 10 PANAS items and asked to respond with how they

currently felt in the present moment using a Likert scale of 1 (“Not at all”) to 5 (“Extremely).”

The participants were administered a prompt with the following instructions: “Please try your

best to concentrate on the task. Your performance may be compared anonymously with other

participant’s performance at a later time.” Pressing the “next” button on the screen confirms the

participant has seen the instruction. Instructions were then provided on how to complete the

training, (i.e., “Focus your gaze on the cross. You will briefly see two stimuli. Tap the half of the

screen where the dot appears next as promptly as you can!”) Participants completed the training

session. Each training session consisted of 240 trials. Participants completed a total of 8,640

trials during the 6-week experiment. Feedback on response accuracy (correct vs. incorrect) and

response time (“Fast”, under 300 ms; “OK”, between 300 – 1000 ms; “Too Slow”; above 1000

ms) was given following each trial.

As mentioned previously, participants were assigned to two groups: the ABM treatment

group or the neutral control group. The cell phone application functioned identically in both

groups, although trial type differed between groups. Incongruent trials (i.e. trials where the dot

appeared behind the neutral stimuli) were administered to participants in the ABM treatment

23

group. This was done in order to modulate attention training away from threatening stimuli. The

control group was administered an equal amount of incongruent and congruent trial types in each

training session. Participants were able to track their progress and current accuracy during each

session. Progress was also monitored by participants in the CABIN lab to ensure sessions were

being completed appropriately. After the six weeks are completed, participants were scheduled

for their second EEG session and compensated $65 for their time spent on the study.

Following completion of the first EEG, participant’s application ID and pin were

implemented into the app., giving them access to begin training. They were given reminders to

complete their sessions on a daily basis and were sent warning messages when sessions were not

completed in the appropriate time. Participants were required to complete six test sessions via the

cell phone app each week, for six consecutive weeks. No more than three sessions were allowed

to be completed in one day, preferring the completion of one session each day, six days of the

week.

Since data was collected from a larger research project (NIMH R15MH1109051),

participants were required to get a pre and post fMRI, 10 minute resting state measure in addition

to the two EEG sessions and completion of the six week training in order to receive

compensation. Some participants in the treatment condition were offered increased compensation

to return for a third EEG, fMRI, and behavioral measure (i.e., dot-probe task, STAI, DASS, and

CERQ) session 6 weeks following post session data collection which was added two years after

beginning data collection. Participants received a total of $65 or $100; specific to whether they

returned for third session data collection (receiving $100 in comparison to $65 for completion of

the original experiment procedure).

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EEG recording.

In this experiment, computer program setup was predefined in a protocol (See Flanker

Protocol in Appendix). Data were continuously recorded using a 64 channel Geodesic Sensor

Net (see figure 2; Electrical Geodesics Inc., Eugene, OR) with AgCl electrodes placed according

to the international 10-20 system. Signal acquisition was performed using Net Station 4.5.4

software (Electrical Geodesics Inc., Eugene, OR) and was digitized at a sampling rate of 500 Hz.

Electrode impedance was kept below 75 kilo-ohms.

Data processing.

EEG data was analyzed using EEGLAB V2019.1 (Delorme & Makeig, 2004) and

ERPLAB V8.0 (Lopez-Calderon, J., & Luck, S. J., 2014)., MatLab packages (2010, The

Mathworks Inc., Massachusetts, USA). Continuous EEG data set files were first iterated through

a preprocessing script. Each file was low-pass filtered at 30 Hz and high-pass filtered at 0.1 Hz.

Congruent and incongruent bins were time-locked to the stimulus presentation and epoched 200

ms prior to presentation and 800 ms post presentation and were then baseline corrected from -

200 ms to 0 ms. Files then underwent artifact detection where amplitude deflections of at least

140 μV at eye-blink electrodes were considered eye-blinks and amplitude deflections of 55 μV

or greater at eye-movement electrodes were considered horizontal eye-movements. Epochs

containing eye-blinks or eye movements were excluded from data analysis. Additionally, epochs

with more than 10 bad channels were discarded. Bad channels were replaced with interpolated

data using spherical splines from the remaining channels. Following interpolation, files were

subjected to an Independent Components Analysis (ICA) to identify and correct artifacts. The

25

ERP segments were then averaged for each participant so that each electrode had a single

waveform for each of the four conditions (pre-control, pre-training, post-control, post-training).

Following preprocessing, electrode averages amongst each subject were re-referenced

within groups and grand averaged. The data was then extracted and saved to an untraceable ID

number associated with each participant (Larson & Clayson, 2011).

Following data processing, the grand averaged frontocentral electrode waveforms of the

four groups were visually inspected in order to determine which electrode best displays the delta

N2. Statistical analysis utilized the amplitude extraction from 300 - 375 milliseconds of the

individual averages across groups.

Analytic Plan

Statistical analyses were performed using SPSS V21 (2012, IBM Statistics, New York,

USA).

Hypothesis 1: heightened levels of trait anxiety are negatively correlated with ΔN2 amplitudes.

To test the correlation between pre-ABM self-reported trait anxiety scores and ΔN2

amplitudes, a Pearson Correlation was used. A negative correlation between pre-ABM trait

anxiety scores and ΔN2 amplitudes would lend support towards hypothesis 1. It was

hypothesized that participants’ self-reported anxiety levels would be negatively correlated with

their ΔN2 amplitudes as measured by the STAI questionnaire and EEG respectively. That is,

participants who reported having higher anxiety displayed a larger negative ΔN2 (ΔN2 =

incongruent flanker N2 - congruent flanker N2). These results would provide evidence that

enhanced emotional processing mechanisms, present in anxious individuals, were interfering

with or disrupting the cognitive mechanisms that are engaged when monitoring conflict.

26

Hypothesis 2: 6-weeks of ABM training decreases self-reported anxiety levels and ΔN2

amplitudes.

To test for changes in self-reported trait anxiety following ABM training and changes in

ΔN2 following training, a 2 (ABM vs Control) × 2 (pre vs post) mixed ANOVA was performed

on anxiety and ΔN2 measures. It was hypothesized that there would be a significant interaction

for both ANOVAs demonstrating significant changes in ΔN2 amplitudes and self-reported

anxiety following training for the ABM group only. More specifically, post-session ΔN2

amplitudes, as measured with EEG, were expected to significantly decrease in the training group

compared to their pre-ABM training amplitudes. That is, the ΔN2 amplitudes of the training

participants is less negative following ABM training. Additionally, the self-reported anxiety

scores of the training participants is reduced compared to their pre ABM training scores.

Furthermore, the quantity of reduction between participants' ΔN2 amplitude and anxiety scores

were expected to be proportional across the training sample group. This suggests an intimate

relationship between AB, conflict monitoring, and anxiety.

27

RESULTS

N2 Manipulation Check

Following the statistical processing of the results, a N2 Manipulation Check was

implemented (i.e., test for amplitude differences between congruent and incongruent flanker

trials). Here, a statistical significance was documented for pre (Mdiff = 2.72µV, SD = 0.57), t (26)

= 4.781, p < 0.001 and post ΔN2 amplitudes Mdiff = 2.84 µV, SD = 0.49), t (26) = 5.828, p < .001.

Hypothesis 1: heightened levels of trait anxiety are negatively correlated with ΔN2

amplitudes

A Pearson correlation was used to test for a relationship between self-reported levels of

anxiety and ΔN2 amplitudes. There was not a statistically significant correlation between pre-

training STAI-T scores (M = 52.07, SD = 6.66) and pre training ΔN2 amplitudes (M = -2.72 µV,

SD = 3.01 µV), r (27) = 0.11, p = 0.59).

Hypothesis 2: 6-weeks of ABM training decreased anxiety levels and ΔN2

In order to test for the effect of ABM training on anxiety levels and ΔN2 amplitudes, a 2

(pre-training vs. post-training) × 2 (ABM vs. control) mixed factorial ANOVA was implemented

for both STAI-T scores and ΔN2 amplitudes. The between subject measure used was the type of

treatment group (control vs. ABM) with the repeated measures independent variable was time

(Pre and Post). The dependent variables tested were ΔN2 amplitude and STAI-T scores.

There were not significant main effects of time; pre-training (M = -2.72 µV, SD = 3.01

µV) and post-training (M = -2.84, SD = -2.58), F (1, 26) = 0.048, p =.828, 𝜂𝜂2 = 0.002, or group,

28

F (1, 26) = 0.095, p = 0.761, 𝜂𝜂2 = 0.004, on ΔN2 amplitudes. Additionally, there was no

significant interaction between the time × group on the ΔN2, F (1, 26) = 0.228, p =.637, 𝜂𝜂2 =

0.009 (Experimental Group pre: M = -2.71 µV, SD = .821µV; post: M = -2.57, SD = .698, and

Control Group pre: M = -2.74, SD = .821; post: M = -3.11, SD = .698)

For the STAI-T scores; there was not a statistically significant effect for time in self-

reported anxiety; pre-training (M = 52.07, SD = 6.67) and post-training scores (M = 52.19, SD =

8.2), F (1, 25) = 0.004, p = 0.952, 𝜂𝜂2 = 0.000. The tests of between-subjects group effect was

also not significant; (pre: M = 52.77, SD = 5.372; post: M = 52.00, SD = 7.714), F (1, 25) = .035,

p = .853, 𝜂𝜂2 = .001. Finally, the training × group interaction was not significant, F (1, 25) =

0.418, p = 0.524, 𝜂𝜂2 = 0.016.

29

Discussion

The main objective of this experiment was to assess the effects of ABM training on ERP

modulation and self-reported levels of anxiety in attempts to strengthen the connection between

electrocortical changes and anxiety levels following ABM. As documented in the results, there

was no significant correlation between pre-STAI-T scores and pre-ΔN2 amplitudes.

Additionally, there was no effect of training on STAI-T scores or ΔN2 amplitudes. This suggests

that there is no relationship between ABM training on anxiety reduction or ΔN2 modulation.

Prior to analyzing the ΔN2 amplitudes and the STAI-T scores, a N2 manipulation test was

conducted and provided evidence that there was a significant difference between incompatible

and compatible waveform amplitudes.

ΔN2 and Self-Reported Anxiety

The was a non-significant Pearson correlation between pre ΔN2 amplitudes and

participants self-reported anxiety. This does not support the hypothesis that there exists a

significant negative relationship between pre ΔN2 amplitudes and the STAI-T (i.e., an increase

in STAI-T associated with heightened negative N2 amplitudes). This is inconsistent with

previous research where the ΔN2 was found to be amplified in individuals experiencing greater

levels of anxiety (Dennis and Chen, 2008; Beste et al., 2010; Perez-Edgar et al., 2016; Baldwin

et al., 2013). An increased (more negative) ΔN2 has been correlated with heightened cognitive

processing; the amplitude is increased when the task displays conflict (incongruent trial during

flanker task, i.e., linked to inhibition; Larson & Clayson, 2011) or when a stimuli is highly

salient (Eldar and Bar-Haim, 2010). As mentioned previously, individuals with increased anxiety

30

have an AB to threatening stimuli, resulting in decreased cognitive control. When an individual’s

cognition is diminished, it inhibits their ability to orient to goal-relevant stimuli, facilitating

one’s inability to implicitly regulate emotions (Etkin & Schatzberg, 2011). Therefore, cognitive

control and inhibition are strongly correlated with conflict processing. Furthermore, because the

ΔN2 is associated with increased conflict processing, it was expected that a more negative ΔN2

amplitude correlates with pre-STAI-T scores.

The lack of significance toward the first hypothesis could be due to the small sample size

(N = 28) not providing enough power to detect a significant effect; a larger sample size may have

provided more evidence of a relationship between increased self-reported anxiety and the ΔN2.

Another reason could be due to the self-reported survey measuring for anxiety (STAI-T). Self-

reported surveys can sometimes provide inaccurate conclusions due to how the question is

perceived or the current state of the participant. Because inclusion criteria for participant

screening required both a heightened sense of anxiety(as measured with the STAI-T)—and the

dot-probe scores, it is less probable that the screening alone may have influenced the

participant’s perceptual framing, but may have had an effect on correlation performed above

(Tversky, A. and Kahneman,D., 1981).

A study conducted by Owens et al. (2015) looked into the effect of cognitive control

deficits modulated by task demands and emotional salience through a modified version of the

Erikson Flanker task (Eriksen & Eriksen, 1974). Participants were displayed five faces

simultaneously in the middle of the screen for each trial. The middle (third) face was the central

target and consisted of a neutral male or female face. The other four faces surrounding the

central target (two on each side) were faces displaying angry, happy, or neutral expressions; the

surrounding faces were of the opposite sex as the central target (i.e., if the central neutral face

38

was a female, the surrounding four faces were male). The participants were instructed to

determine the gender of the central target as quick and accurately as possible. Concurrent with

the flanker task, participants were presented with an auditory tone of low, mid, or high pitch

during each trial. This task was split into two conditions; low-load and high-load. During the

low-load condition, instructions were to say the word “tone.” In the high-load condition,

participants were instructed to report the pitch of the tone. In both conditions, participants were

to respond after the facial stimuli from the flanker task is displayed. In order to assess the effects

of self-reported anxiety and worry, participants were administered the STAI, the Worry Domains

Questionnaire (WDQ), & the Penn State Worry Questionnaire (PSWQ) prior to beginning the

experiment. Participants displayed larger ΔN2 amplitudes during the high-load condition

compared to the low-load condition, suggesting that increased need for cognitive resources is

linked to an increased N2. Additionally, trait worry was associated with increased ΔN2

amplitudes under the high-load condition. The use of the WDQ and the PSWQ in addition to the

STAI may provide more insight into the variability of individual levels of anxiety and worry.

Here, converse to the results provided above, a significant correlation between ΔN2 amplitudes

and self-reported anxiety--or in this case--trait worry, was documented. The increased cognitive

component may be a necessary variable to test N2 modulation.

As mentioned previously, the N2 is a negative ERP waveform associated with conflict

processing (i.e., increased conflict correlates with a more negative ΔN2 amplitude; Nieuwenhuis

et al., 2003;Yeung et al., 2004). When participants completed the flanker task (see figure 1), the

incongruent trials (< < > < <) elicited a more negative amplitude compared to congruent (< < < <

<) trials, reflecting the electro-cortical engagement of top-down control to decrease conflict.

(VanVeen & Carter, 2002; Yeung et al., 2004). Furthermore, N2 amplitudes are influenced by

39

the exposure to preceding trial condition. Such that, when an incongruent trial precedes an

incongruent trial, the N2 is diminished in comparison to a congruent trial preceding an

incongruent trial. The same is true for a congruent trial that is preceded by another congruent

trial (Clayson & Larson, 2011a; Clayson & Larson, 2011b). This is referred to as repetition

priming, and is understood to be the unconscious conditioning to specific patterns during

performance tasks (e.g., the flanker task). This phenomenon could be involved in the alteration

of data by diluting ERP amplitudes. For example, if a participant were to elicit an N2 in response

to two incongruent trials back to back, there is a probability that the N2 elicited during the

second trial becomes slightly diminished. A possible explanation could be that the neural

mechanisms involved in repetition priming recruit distributed neural resources that improve

cognitive functioning, in turn reducing conflict processing. If conclusive, this could lend

evidence to why the ΔN2 between conditions are relatively similar and could potentially

diminish the correlation between participant’s pre-STAI-T scores and the ΔN2.

Effect of ABM Training on Self-Reported Anxiety and ΔN2 Amplitude

There was no significance found between pre and post self-reported STAI-T or pre and

post ΔN2 amplitudes. Pre STAI-T scores for all participants had almost no change when

compared to post STAI-T scores. The experimental group displayed similar results between the

pre STAI-T scores and post STAI-T scores. Therefore, six weeks of ABM training did not have a

significant effect on decreasing trait anxiety, as determined by their STAI-T scores. One possible

reason is that although there is a large amount of evidence linking anxiety with AB, anxiety

disorders are diagnosed without any altered AB to threat. The ABM training is used to transfer

attention away from threatening stimuli in order to increase cognitive functioning and decrease

stimulus driven behaviors; this increases one’s ability to utilize goal-oriented behavior. If

40

participants were experiencing anxiety without a perception of threat, ABM would not be

sufficient to decrease their symptoms because the anxiety is not associated with increased

attention to threatening stimuli. Conversely, previous research has shown that there may be a

time lag between the conclusion of ABM training and both the electro-cortical and behavioral

modulation (Browning et al.2012). This may provide another reason that behavior and electro-

cortical activity were not shown to have been significantly modulated.

When comparing ΔN2 amplitudes, there was no significant relationship between pre and

post sessions. There was not much of a change between pre ΔN2 (M = -2.72 µV) and post ΔN2

(M = -2.84 µV) amplitude when comparing all participants (regardless of group), which was

expected due to only half (N = 14) of the participants receiving ABM treatment. Interestingly,

there was a slight increase (less negative) in the group receiving ABM when comparing pre ΔN2

amplitudes (M = -2.71 µV) and post ΔN2 amplitudes (M = -2.57 µV). Although there was no

significant change between these amplitudes, the data still displays a trend of ABM on ΔN2

amplitude, providing some evidence that ABM may be able to improve task performance during

increased cognitive conflict. This would need to be assessed in a larger sample in order to

properly examine the influence of ABM on the ΔN2. Further, the ΔN2 plays a large role in the

allocation of top-down cognitive control, impairing one’s conflict processing (i.e., when your

brain is competing for goal-driven and stimulus driven properties; vanVeen & Carter, 2002;

Yeung et al., 2004). The N2 measures conflict through the detection of novelty vs mismatch

stimuli when stimuli are attended to (Na¨a¨ ta¨nen & Gaillard, 1983; Na¨a¨ ta¨nen & Picton,

1986). Implementing additional variables to increase the cognitive load might improve or

establish a relationship between ABM and the ΔN2.

41

Other studies looking at the effect of ABM on the N2 have used No-go and flanker

paradigms. From this experiment, the results suggest that ABM does not decrease anxiety over a

six-week period, at least when using self-reported measures such as the STAI-T. In parallel with

other studies, this experiment suggests that ABM may strengthen cognitive functioning by

assisting in the reorientation of attention to goal-directed stimuli measured by the ΔN2 (Larson et

al., 2013). Although, the results were not significant, the present study did see a small effect of

ABM on post ΔN2 amplitude that could be replicated and expanded on to further assess its

reliability/validity.

Clinical Implications

The results from this study alone do not provide enough evidence to justify the usage of

the N2 as a target for treatment efficacy. When comparing the pre (M = -2.71) and post ΔN2 (M

= -2.57) averages, there was a slight increase (less negative) in the average post ΔN2 amplitude

in the experimental group. Although, the results were not significant, the results may suggest that

with more participants and further research, ABM could have a noteworthy effect on anxious

symptomology.

There was no effect of ABM on self-reported anxiety measures. The pre STAI-T scores

(M = 52.57) showed almost no difference compared to the post STAI-T scores (M = 52).

Although early ABM studies documented promising results for improving anxiety, (Mogg &

Bradley, 2016), these results do not support the justification for clinical application. As

mentioned previously, not all individuals diagnosed with anxiety experience an AB to threat

(Van Bockstaele et al, 2014). For example, a study conducted by Eldar et al (2012), assessed the

relationship between anxious children and ABM-threat avoidance. This excluded almost half of

the population of anxious children, due to the absence of pre-existing threat bias. This is an issue

42

because although this treatment may be beneficial for anxious children (& adults) who do have a

pre-existing bias to threat, it is simultaneously neglecting the many others experiencing the same

or similar mental illness without a bias to threat. Another approach of ABM is referred to as

ABM-positive search training. In contrast with the ABM-threat avoidance model, positive search

training directs your attention toward positive stimuli in comparison to orienting away from

negative (Dandeneau et al., 2007; De Voogd et al., 2014; Waters et al., 2013; Waters et al., 2015;

Waters, et al., 2016). This approach was designed to attempt to suit all individuals with anxiety

disorders, through the instruction to fixate on the positive images, while inhibiting background

stimuli (Dandeneau et al., 2007; De Voogd et al., 2014; Waters et al., 2013; Waters et al., 2015;

Waters, et al., 2016). Moreover, a recent paper published by Sylvain et al. (2020) investigated

the effects of a single session ABM to positive stimuli on the electro-cortical activity relating to a

reward processing ERP complex, P2-RewP-P3 (Sylvain et al. 2020). The investigation

demonstrated a significant effect on female P2 amplitudes following positive ABM training. If

more studies used the ABM positive search approach, researchers may find increased success of

ABM in the clinical setting.

Limitations & Future Directions

There are a few limitations to this study. As mentioned previously, the data from this

project were derived from a larger grant (#R15MH1109051). The grants original ERP of interest

was the ERN and the first 29 participants were not flagged for the N2 during the EEG

flanker/resting state measure. This provided a small sample (N = 28), diminishing the statistical

power. The study lost 18 participants due to attrition. If participants did not finish the

experiment, post-data ΔN2 amplitudes and behavioral measures did not exist, forcing the

removal of such participants from analysis. The present study lost an additional 33 participants

43

due to the emergence of the virus COVID-19, imposing an executive order to close labs and keep

the participants safe and healthy. Due to this, behavioral measures were altered to an online

format to continue collection of attainable data. Post EEG measures were not conducted due to

mandated quarantine and the closure of all school buildings/labs. This caused the exclusion of

many participants from analysis of this thesis. The small sample (N = 28) and within groups

(experimental = 14; control = 14), was a major limitation in this study.

Another limitation included the restriction of anxiety symptomology to the self-reported

STAI-T surveys. Self-reported measurements attempt to objectify subjective information and can

be influenced by a participant’s mental state. To improve or reduce inaccurate classification,

additional questionnaires, such as the Worry Domains Questionnaire (WDQ) and the Penn State

Worry Questionnaire (PSWQ) could be used.

An additional limitation of this study (as mentioned in the Effect of ABM training on self-

reported anxiety and ΔN2 amplitude section) included the possibility that the reliability/validity

of the dot-probe measure was suspect (Kappenman et al., 2014; Schmukle, 2005; Staugaard,

2009; Waechter et al., 2013). The dot probe paradigm was developed to quantify an individual’s

attention bias to threatening stimuli (please refer to the ABM section in the literature review for

more information on this measure). In an experiment conducted by MacNamara et al. (2013),

participants displayed a heightened attentional focus towards threatening stimuli in numerous

measures. Due to possible poor psychometric properties of the reaction time (RT) measure of the

dot-probe task (Schmukle, 2005; Staugaard, 2009), it is possible that some dot-probe measures

do not provide an accurate analysis (quantification) of the RT bias measure. For example, the

dot-probe task has been shown to have poor test-retest reliability (Schmukle, 2005; Staugaard,

2009), as well as reduced internal reliability (Kappenman et al., 2014; Schmukle, 2005;

44

Staugaard, 2009; Waechter et al., 2013). In regards to this thesis, it is possible that participant’s

AB scores were invalid due to the imprecision of the dot-probe measurement. Therefore,

implementing ABM treatment for participants that experience anxious symptomology, but do not

experience an enhanced attention bias to threat, would be clinically insufficient (Van Bockstaele

et al., 2014). Interestingly, Reutter and colleagues (2017) demonstrated significant internal

reliability when measuring attention bias to threatening images with the N2pc ERP in the dot-

probe task. Moreover, they provided evidence that there was a correlation between more

negative N2pc amplitudes and more severe symptoms of anxiety, as measured by the SPI and

STAI (Reutter et al., 2017). Future research incorporating the dot probe test may benefit by

looking at and comparing the N2pc amplitudes in substitution of the RT measures.

Furthermore, resent investigations documented evidence that a pooled FCz/Cz electrode

statistical analysis of N2 amplitudes provide a more complete picture (Iannaccone et al., 2015).

Here, the statistical analysis included the FCz electrode solely and pooling both electrode sites

might be a direction for future investigations.

Conclusions

This thesis sought to test the effect of ABM on trait anxiety and electro-cortical

modulation as quantified by the STAI-T questionnaire and the ΔN2, respectively. The study

investigated these effects by virtue of a multi-session mobile training application that featured

equally valenced stimuli for both control and training groups. The filtered inclusion of

participants provided a sample that had both high levels of trait anxiety and a preexisting

attention bias to threatening stimuli. Although the results did not lend significant support towards

the hypotheses, the investigation bore insight into future avenues of inquiry. Specifically, the

implementation of additional questionnaires such as the Worry Domains Questionnaire (WDQ)

45

and the Penn State Worry Questionnaire (PSWQ) could impart a more granular classification of

associated symptomologies to which an analytical comparison may furnish deeper insights.

Moreover, implementing a flanker paradigm that induces variability in cognitive load may

invoke differences between N2 activity elicited from congruent and incongruent trials that here

has been deemed negligible. In spite of the limitations addressed and the diminished expected

sample size, as a result of an ensuing global pandemic, the investigation demonstrated a subtle

decrease in the post-ΔN2 amplitude for the training group compared to their pre-ΔN2

amplitudes.

46

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Appendix A

Figure 1. Example of the modified flanker task paradigm.

Figure 2. 10-20 Electrode map highlighting FCz (electrode 4)

Negative

Neutral

1000 ms 100 ms Until Response 1000 msse

Figure 3: Example of the dot-probe task during ABM training displaying a threatening (left) and neutral face (right).

+

+ +

+

+

+

65

Figure 4. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the control group (PRE data).

Figure 5. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the control group (POST data).

66

Figure 6. Figure 4. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the experimental group (PRE data; i.e., pre 6-week ABM training).

Figure 7. FCz ΔN2 (circled) grand average waveform (with congruent [red] and incongruent amplitudes [blue] for the experimental group (POST data; i.e., post 6-week ABM training).

67

Figure 8. Correlation between pre-training self-reported STAI – T score and pre-training ΔN2 amplitude (in µV) across all participants.

Figure 9. Correlation between pre-training self-reported STAI – T score and pre-training incompatible ΔN2 amplitude (in µV) across all participants.

Correlation between pre-STAI scores and Pre ΔN2 amplitudes 80

60

40

20

0 STAI-T scores

Differences in STAI-T scores comparing pre vs post ABM 60

55 50 45 40 35 30 25 20 15 10

5 0

52.57142857 52

Pre ABM

Post ABM

1 Pre vs. Post ABM training

Pre

ΔN2

Inco

mpa

tible

(a

mpl

itude

s in

µV)

Self-

repo

rted

STA

I-T sc

ores

Figure 10. Evidence of the correlation (or lack thereof) between pre-training STAI-T score and pre-training ΔN2 amplitudes (in µV) in the experimental group.

68

Figure 11. Although not significant, this chart provides evidence that ABM may correlate with the ΔN2, suggesting it may be related to conflict monitoring.

Pre ΔN2 amplitudes pre vs. post ABM training

-2.8

-2.7

-2.6 Pre-ABM

Post-ABM

-2.5

-2.4 Session (pre vs post)

ΔN2

ampl

itude

s in

µV

NORTHERN MICHIGAN

UNIVERSITY

OFFICE OF GRADUATE EDUCATION AND RESEARCH

1401 Presque Isle Avenue Marquette, "11 49855-5301

906-227-2300906-227-2315

www.nmu.edu/graduatestudies MEMORANDUM

TO:

DATE:

FROM:

RE:

Joshua Carlson Lin Fang Department of Psychological Science

Taylor Susa Hayley Gilbertson Katie Elwell Haley Gaboury Justine Nelson Jacquie Cammarata Hannah Kaull Natalie Strand Nathan Schmitz Mason Steinhauer Madeline Voltz Diego Ulloa Abigail Schaedig Abbey Zigarac Analise Osgood

Sep tern bee 5, 2019 \ cjJ Lisa Schade Eckert 'o. Dean of Graduate Education and Research

Modification to HS 13-555 Original !RB Approval Date: 11/12/2013 Modification Approval Date: 9/5/2019 "Characteristics of Attention Bias Modification - New Title: "R 15MH 1109051: Neuroplasticity in an Extended Amygdala Network as a Target Mechanism for Attention Bias Modification Outcome"

Your modification for the project "Characteristics of Attention Bias Modification - New Title: "RI 5MH 1109051: Neuroplasticity in an Extended Amygdala Network as a Target Mechanism for Attention Bias Modification Outcome" has been approved by the Northern Michigan University Institutional Review Board. Please include your proposal number (HS 13-555) on all research materials and on any correspondence regarding this project.

Any additional personnel changes or revisions to your approved research plan must be approved by the IRB prior to implementation. Unless specified otherwise, all previous requirements included in your original approval notice remain in effect.

If you have any questions, please contact the IRB at hsrr@nmu.edu.

Appendix B

69