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NEUROBEHAVIORAL SIGNATURES INCHILDREN-VICTIM OF BULLYINGIsabel SolisUniversity of New Mexico

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Recommended CitationSolis, Isabel. "NEUROBEHAVIORAL SIGNATURES IN CHILDREN-VICTIM OF BULLYING." (2018).https://digitalrepository.unm.edu/psy_etds/257

Isabel Solis Candidate Psychology Department This thesis is approved, and it is acceptable in quality and form for publication: Approved by the Thesis Committee: Kristina R. Ciesielski, Ph.D., Chairperson Bruce Smith, Ph.D. Julia Stephen, Ph.D. Steve Verney, Ph.D.

NEUROBEHAVIORAL SIGNATURES

IN CHILDREN-VICTIM OF BULLYING

by

ISABEL SOLIS

B.S., PSYCHOLOGY, UNIVERSITY OF NEW MEXICO, 2012

THESIS

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

Psychology

The University of New Mexico

Albuquerque, New Mexico

July, 2018

ii

DEDICATION

To my mother, Silvia Vargas, who has always shown me what a strong woman looks like. Thank you for all of your hard work and sacrifices. I am truly blessed to be your daughter.

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ACKNOWLEDGEMENTS

First and foremost, I praise God for making the impossible possible. Next, I would like to thank my faculty mentor Dr. Kristina Rewin Ciesielski for all her time, guidance and unwavering support. Her dedication to children and science is inspiring and I will forever be grateful to be her student. I also wish to thank my thesis committee members, Dr. Julia Stephen, Dr. Bruce Smith, and Dr. Steve Verney, for providing invaluable support during this process. I sincerely appreciate their time and interest not only in this study but in my professional development. I would also like to thank Dr. Paul Lesnik for his assistance with the statistical analysis. Next, I would like to thank my research assistants Charlene McGinnis and Yesol Kim for all of their time, hard work, and dedication. I really could not have done this project without their assistance. I am deeply grateful to all the children and families who participated in this study. Their willingness to be part of this work is truly motivational. Finally, I would like to thank my family for all of their encouragement and endless love during this process.

iv

NEUROBEHAVIORAL SIGNATURES

IN CHILDREN-VICTIM OF BULLYING

by

Isabel Solis

B.S., Psychology, University Of New Mexico, 2012

M.S., Psychology, University Of New Mexico, 2018

ABSTRACT

Experiencing bullying victimization can lead to detrimental damage to a child’s

life potential, reduced long-term contributions to society, and in severe cases, to suicide

or desperate acts of defensive aggression, such as school shootings. The current study

aimed to examine risk factors that may increase the vulnerability of a child to become a

target of bullying victimization and the related consequences of victimization, using

rigorous neuropsychological and EEG measures. The end-goal is to translate these

findings into a program of preventive intervention increasing the child’s resilience and

improving social culture among youth in the academic environment. We propose a two-

component novel model for examining child-victim characteristics: Trait Signatures, as

crystalized, long-term neurobehavioral and brain neurophysiology (EEG) markers and

State Signatures, the psychological, somatic, and cognitive acute consequences of

bullying victimization. Results from 16 Children-Victim (VC) and 16 non-exposed to

bullying, Control Children (CC), ages 6-17 are presented. Our key findings in VC as

compared to CC show: In Trait Signatures significantly higher scores on measures of

v

anxiety and compulsivity, significantly reduced skills in visual-spatial perception and

attention, but no significant group differences in oscillatory brain activity (alpha power)

during visual cognitive tasks demanding top-down inhibitory control of response. In State

Signatures significantly higher levels of depressive moods, significantly reduced

visuospatial memory and visually mediated conceptualization, and a significantly higher

rate of life-long traumatic experiences, including bullying victimization. Our findings

implicate a prime significance of emotional-social difficulties in VC that may impact

visual cognitive proficiency in complex social problem-solving interactions. The

identified emotional-social-cognitive composite of difficulties in VC must be addressed

in future studies and in prospective programs of prevention.

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TABLE OF CONTENTS

LIST OF FIGURES................................................................................................... viii

LIST OF TABLES ......................................................................................................ix

LIST OF ACRONYMS ................................................................................................ x

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

Definition of Bullying ......................................................................................... 1

New Model of Children-Victim: Trait Signatures and State Signatures ............... 2

Reported Consequences of Bullying.................................................................... 2

Report on Etiology of Being a “Bully” or a Child-Victim ................................... 9

Aims & Hypotheses ...........................................................................................15

METHODOLOGY ......................................................................................................16

Participants ........................................................................................................16

Measurements/Tasks ..........................................................................................16

Data Analysis ....................................................................................................22

RESULTS ....................................................................................................................25

Demographics ....................................................................................................25

Trait Signatures .................................................................................................28

State Signatures .................................................................................................33

DISCUSSION ..............................................................................................................37

Study Limitations ..............................................................................................41

Summary ...........................................................................................................42

APPENDICES .............................................................................................................43

APPENDIX A - Trait Signatures: Neurobehavioral Measurements .........................43

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APPENDIX B - Trait Signatures: Resting State Alpha Power ..................................44

APPENDIX C - Trait Signatures: Neurobehavioral Correlations in Control

Children .......................................................................................................................45

APPENDIX D - State Signatures: Neurobehavioral Measurements .........................46

REFERENCES ............................................................................................................47

viii

LIST OF FIGURES

Figure 1. Schematic of Blue Man-Stop Response Task ..................................................19

Figure 2. Contrasts in Trait Signatures between Children-Victim and Control Children .29

Figure 3. Alpha Power Correlograms.............................................................................31

Figure 4. Contrasts in Neurocognitive State Signatures between Children-Victim and

Control Children............................................................................................................34

Figure 5. Contrasts in Clinical State Signatures between Children-Victim and Control

Children ........................................................................................................................35

ix

LIST OF TABLES

Table 1. Participant Demographics ................................................................................27

Table 2. Trait Signatures: Alpha Amplitude for BM-SRT ..............................................30

Table 3. Trait Signatures: Neurobehavioral Correlations in Children-Victim .................32

Table 4. State Signatures: Neurobehavioral Correlations in Children-Victim .................36

x

LIST OF ACRONYMS

ASD Autism Spectrum Disorders BM-SRT Blue Man–Stop Response Task BVS-I Bullying Victimization Severity Index (IBS, History of bullying victimization) CC Non-bullied, Control Children CDRS-R Children’s Depression Rating Scale-Revised CFT-C Rey-Osterrieth Complex Figure Test– Copy CFT-DR Rey-Osterrieth Complex Figure Test– Delayed Recall CY-BOCS Child Yale–Brown Obsessive-Compulsive Scale FAS Benton Oral Word Fluency Test/FAS H-DSM History of DSM-5 Disorders IBS Illinois Bully Scale LF Left Frontal Region LP Left Parietal Region NIH-FICA NIH Toolbox - Flanker Inhibitory Control and Attention Test OCS OC-Spectrum Disorders OCD Obsessive-Compulsive Disorder PTSD Posttraumatic Stress Disorder QTE Questionnaire on Traumatic Experiences in Early Childhood RF Right Frontal Region RP Right Parietal Region SCQ Social Communication Questionnaire SI-I Social Interactions Index (SCQ, Peer Interaction History) SS State Signatures SS-PV Current state of peer victimization TS Trait Signatures VA-I Visual Attention Index (CFT-C, NIH-FICA) VC Children-Victim of bullying VE-I Verbal Expression Index (WASI-S, FAS) VS-I Visuo-Spatial Index (CFT-DR, WASI-BD, WASI-MR) WASI-BD Wechsler Abbreviated Scale of Intelligence-II – Block Design WASI-MR Wechsler Abbreviated Scale of Intelligence-II – Matrix Reasoning WASI-PRI WASI-II Perceptual Reasoning Index WASI-S Wechsler Abbreviated Scale of Intelligence-II – Similarities WASI-V Wechsler Abbreviated Scale of Intelligence-II – Vocabulary WASI-VCI WASI-II Verbal Comprehension Index

1

INTRODUCTION

Definition of Bullying

Bullying has considerably increased in American schools within the last two

decades, resulting in physical and psychological harm to the young victims’

development, including loss of personal potential, school shooting tragedies, increased

risk of suicide and severe family distress (Nishina, Juvonen & Witkow, 2005; Ranney et

al., 2016; Srabstein, 2013; Rigby & Slee, 1999). Bullying is an aggressive and

persistently repeated act of behavior with an intention to harm, and primarily related to

social power differential (Olweus, 1978; 1993). Forms of bullying include social

aggression (e.g., spreading rumors, social exclusion), verbal aggression (e.g., threats,

name calling), physical aggression (e.g., pushing, hitting), and cyber bullying (e.g., using

electronic means to hurt the reputation of another person; Olweus, 1993). Bullying

transcends across age, gender, race/ethnicity and SES, and among all other environments,

is most frequently recorded in academic institutions.

Bullying occurs in 30% of children and adolescents in the United States (Nansel

et al., 2001; Wang et al., 2009). Other industrialized countries (e.g., Germany, United

Kingdom, Spain, Australia) report a similar prevalence, ranging from 20-30% (Zych et

al., 2017). Importantly, prevalence rates increase among children with special healthcare

needs, as seen in children with Autism Spectrum Disorder (ASD). Their rates of

victimization range from 46% to 80% of (Cappadocia, Weiss & Pepler, 2012; Sterzing et

al., 2012).

2

New Model of Children-Victim: State Signatures and Trait Signatures

Considering the clinical and social assessment reports below and first

neuroimaging studies on children-victim (VC), we propose a novel model of

investigation of children-victim characteristics: (i) Trait Signatures (TS), i.e., long-term

characteristics of emotional, cognitive and social development related to pre-existent,

permanent changes in basic neural functions underlying top-down inhibitory control of

cognition and emotions; and (ii) State Signatures (SS), as all psychological, somatic and

neuropsychiatric consequences of being a target of bullying, as compared to control

children (CC). Presented below is our original study aiming to define the distinct SS and

TS in the VC population in children between the age of 6-17. Understanding the

significance of different TS will help us to understand the relationship between TS and

SS and predict the effects of bullying on individual children. Our ultimate goal is to

design well informed and effective methods for prevention of bullying victimization.

Reported Consequences of Bullying in Children of Different Age Groups

Bullying rates and forms of aggression differ across developmental stages.

Contrary to popular belief, bullying begins in early childhood (Kochenderfer & Ladd,

1996; Pepler, Jiang, Craig, & Connolly, 2008; Alsaker & Gutzwiller-Helfenfinger, 2010).

Pre-school aged children can identify bullying behavior, rumor spreading and social

exclusion in their peers when assessed with developmentally appropriate methods

(Alsaker & Nägele, 2008). Victims as young as four years of age, have reported higher

levels of somatic complaints and peer problems, whereas, bullies at this age have been

3

more likely to have conduct problems, hyperactivity and poor pro-social behavior (Ilola,

Lempinen, Huttunen, Ristkari, & Sourander, 2016).

Children in elementary school typically report higher rates of bullying compared

to middle school children (Scheithauer, Hayer, Petermann & Jugert, 2006), showing a

peak during school transitions (Pepler et al., 2008). Physical bullying is often reported in

younger children, with the highest reported severity overserved in 8th grade (Scheithauer

et al., 2006), while more sophisticated forms of verbal, social and cyberbullying emerge

during early adolescence (Pepler et al., 2006). However, most of the research has focused

on middle/high school and college students leaving a gap of knowledge in younger

children (Mason, 2013).

It has been documented that early bullying victimization exposure may lead to

long-term negative consequences in adolescence and/or adulthood. Adolescents with a

history of bullying experience report higher rates of depression and emotional problems

(Zwierzynska, Wolke & Lereya, 2013), increased drug and alcohol use (Kim, Catalano,

Haggerty & Abbott, 2011), sleeping difficulties (Fekkes, Pijpers & Verloove-Vanhorick,

2004), and physical problems such as headaches and stomach-aches (Williams,

Chambers, Logan, & Robinson, 1996). Further, Copeland and colleagues (2013) studied

the consequences of childhood bullying in young adults and found that those participants

who experienced bullying during childhood reported higher rates of depressive,

generalized anxiety, panic and agoraphobia disorders in adulthood. Isolation and

exclusion combined with long-term bullying during high school years have been shown

to increase levels of stress and depression during young adulthood (Newman et al., 2005).

4

Gender Related Consequences in Victims of Bullying

Findings regarding gender differences are mixed. Some evidence suggests that

gender differences are found in relation to the forms of aggression used, the frequency of

involvement in bullying and coping strategies. Boys tend to bully both girls and boys and

report higher rates of victimization compared to girls, whereas, girls tend to only bully

girls (Nansel et al, 2001; Veenstra et al., 2005; Ma, 2002). Boys tend to engage more in

physical aggression, while girls engage in verbal aggression (Casey-Cannon, Hayward, &

Gowen, 2001; Nansel et al., 2001). Victimized females tend to internalize behaviors

(Broidy & Agnew, 1997), report higher rates of lower self-esteem, risky sexual behavior

and substance abuse (Bouffard & Koeppel, 2016), while victimized males tend to

externalize behaviors (Broidy & Agnew, 1997). However, when the severity of bullying

increases, gender differences are no longer a significant factor (Rivers & Noret, 2010;

Kim, Boyce, Koh, & Leventhal, 2009; Eisenberg, Neumark-Sztainer & Story, 2003).

Participant Role Related Neurobehavioral Characteristics

Bullying incidences are often thought of as only involving a dyad (i.e., bully and

victim). Those who perpetrate, i.e., target individuals, are identified as bullies, while

those who are the targeted are victims (Olweus, 1978). Bully-victims are those who

engage in both behaviors, but it is uncertain if they have a simultaneous origin or if one

occurred before the other. However, bullying is a phenomenon that involves various

social roles. Salmivalli and colleagues (1996) identified and described, aside from bullies

and victims, additional roles such as assistants, reinforcers, outsiders and defenders.

Assistants are defined as those who join in bullying perpetration but have a secondary

5

role and reinforcers are those who provide positive feedback to the perpetrators or may

be constantly present and not assist the victim. Outsiders keep their distance and watch

from afar and defenders intervene in behalf of the victim. Boys are socially accepted by

their peers when assisting the bully as reinforcers but are typically rejected when acting

as outsiders. However, those who are defenders may be either rejected or socially

accepted by their peers (Salmivalli et al., 1996). Notably, the prevalence rate of bullying

in typically developing children increases to 66% when considering all these roles

(Rivers, Poteat, Noret & Ashurst, 2009).

Three meta-analyses for bullies, victims and bully-victims, examined the

relationship between participation in bullying and psychosomatic complaints in children,

ages 7 to 16 (Gini & Pozzoli, 2009). Results indicated that both victims and bully-victims

share common medical problems, poor relationships with peers, low emotional adjustments

and had the largest effect sizes for psychosomatic problems. All three groups involved in

bullying had a significantly higher risk than in controls for psychosomatic problems,

however, bullies demonstrated the lowest number of medical health problems compared to

victims and bully-victims.

Witnessing bullying or helping in bullying has been linked to elevated mental

health risks (Rivers, et al., 2009). For instance, children who witness victimization and are

not directly involved, have been found to experience cognitive dissonance (Craig & Pepler,

1998). This results from their desire to intervene but not taking any action. Those who have

witnessed victimization report a higher risk for substance abuse (Rivers et al., 2009),

sensitivity to rejection, and neural activity consistent with distress (Masten, Eisenberger,

Pfeifer & Dapretto, 2013).

6

However, the group to display the most significant levels of psychopathology are

bully-victims. These individuals are victimized and display bullying behaviors. They have

the highest risk for suicide attempts, depression and psychosomatic complaints (Rivers &

Noret, 2010; Nansel et al., 2001), medical problems, poor relationships with peers, low

emotional adjustment (Gini & Pozzoli, 2009), substance abuse and hostility (Shepherd,

Sutherland & Newcombe, 2006; Nansel et al., 2001; Juvonen, Graham & Schuster, 2003).

These observations may suggest that bully-victims were originally victims of bullying, and

the course of prolonged abuse developed aggressive behaviors as a mechanism of defense

or hopelessness.

One study found that children who were assessed as bullies and showed theory of

mind (ToM) impairments, were more likely to misinterpret friendly behaviors of others

as aggressive (Van Roekel, et al., 2010). ToM is defined as the ability to predict another

person’s beliefs, intents, desires and knowledge (Baron-Cohen, Leslie & Frith, 1985).

Adolescents with ASD who were victimized, were more likely to misinterpret friendly

behaviors as threatening and bullying (Van Roekel, et al., 2010). This is consistent with

the Victim Schema Model (Rosen, Milich, & Harris, 2007), which states that individuals

who have been victimized, will more often misinterpret non-threatening interactions as

negative or hostile. Thus, bullying becomes a self-sustaining cycle that increases the

probability of future victimization.

Medical and Psychological Consequences of Bullying in Children-Victim

Chronic bullying victimization leads to both severe short- and long-term

consequences in the victim’s physical and psychological health. Prolonged exposure may

7

lead to higher rates of anxiety, depression, reduced academic achievement, suicidal

ideations and completion, uncontrolled acts of aggression and increased likelihood to

become chronically bullied in adulthood (Nishina et al, 2005; Ranney et al., 2016; Rigby

& Slee, 1999; Srabstein, 2013; Sharp, 1995).

Victims and bully-victims have reported higher rates of physical and

psychosomatic health issues such as poor appetite, repeated sore throats, and colds

compared to those who bully and non-involved children (Wolke, Woods, Bloomfield, &

Karstadt, 2001). Exposure to bullying was related to frequent health complaints and

absence from school in young children (e.g., 6 to 9 years of age; Wolke et al., 2001;

Fekkes et al., 2004). Adolescents have reported higher frequency and severity of health

problems (e.g., abdominal pain), higher rates of doctor visits and absenteeism

(Vaillancourt et al., 2011).

Neuroendocrinology studies have suggested that bullying victimization has

neurobiological effects in VC. There appears to be a link between bullying and the

dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis (Knack, Jensen-

Campbell, & Baum, 2011; Vaillancourt et al., 2011; Rudolph, Troop-Gordon, & Granger,

2011). The HPA axis promotes rapid secretion of cortisol to prepare the body for the

fight-or-flight response. Further, bullying victimization appears to be a risk factor for

depressive symptoms in children who displayed heightened anticipatory cortical levels,

especially in girls (Rudolph et al., 2011).

Additionally, childhood bullying exposure predicts low-grade systemic

inflammation in adulthood. Copeland and colleagues (2014) examined low-grade

inflammation by measuring C-reactive protein (CRP) in victims, bully-victims, bullies,

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and non-involved children through a longitudinal study. The authors found a positive

relationship between the number of bullying incidences reported by participants and CRP

levels, thus indicating higher susceptibility to inflammation in victims of bullying.

Prolonged exposure to bullying may also lead to increased risk for severe mood

disorders, psychotic symptoms, behavioral adaptive problems and increased severity of

symptoms in developmental disorders, such as ASD and obsessive-compulsive spectrum

(OCS; Schreier et al., 2009). Bullying victimization has been linked with early onset for

social phobia, OCD, and panic disorder with, or without, agoraphobia (McCabe, Antony,

Summerfeldt, Liss & Swinson, 2003). Schreier and others (2009) found that children who

have been severely or chronically victimized were two times more likely to report

psychotic symptoms even after controlling for prior psychopathology, IQ and other

experienced adversity.

Among the most severe consequences of bullying are suicide attempts and

completions. Victims and bully-victims are at increased risk for suicide ideations and

completion compared to the general population (Kaltaila-Heino et al., 1999; Klomek,

Marrocco, Lkeinman, Schonfeld & Gould, 2007; Rivers & Noret, 2010). Male bully-

victims, ages 12-16, are at the highest risk for suicide attempts (Rivers & Noret, 2010).

Young adults who experienced childhood bullying victimization have reported

significantly higher levels of suicidal ideations compared to the population (Copeland et

al., 2013).

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Neuroimaging Evidence: Consequences of Bullying in Victimized Children

Recent studies have examined bullying in terms of social exclusion in children

and adolescents using neuroimaging techniques. A study using functional magnetic

resonance imaging (fMRI) examined chronically, socially rejected adolescents while

playing a virtual ball-tossing game (i.e., Cyberball; Will, Crone, van Lier & Guroglu,

2016). The authors found that rejection was associated with higher activity in the

subgenual anterior cingulate cortex and the anterior insula during peer exclusion

compared to the control group. These regions have been previously linked with the

distress of peer rejection among adolescents. Additionally, chronically victimized

adolescents were found to exhibit increased activity in the dorsal striatum and lateral

prefrontal cortex compared to CC, when demonstrating prosocial behavior towards other

participants. These regions are associated with cognitive control.

Further, Eisenberger and colleagues (2003) also used the virtual ball-tossing game

and found that those who experienced social exclusion in the game, relative to social

inclusion, showed increased activation in the dorsal anterior cingulate cortex and ventral

prefrontal cortex. Both regions are implicated in functions that warn organisms of a

potential predator or other dangers and to regulate distress associated with physical pain

and negative affect. This suggests a neural overlap between social and physical pain

systems.

Reports on Etiology of Being a “Bully” or a Child-Victim

Clinical and psychosocial analyses of individuals engaging in recent school

shootings have shown that in most cases the performing individual, was not a bully but a

10

victim of bullying. Events occurring at Virginia Tech (CNN Library, 2016), Sandy Hook

Elementary (Sedensky III, 2013) and Umpqua Community College (Loew, 2015) urge us

to look closely at characteristics of those who were engaged in those violent crimes.

These individuals were described by their community as rejected by peers, odd, with

social difficulties, repetitive, strange behaviors and interests (Hughes & Johnson, 2015;

Sedensky III, 2013). Letters left by the offenders described how isolated and

misunderstood they felt (Keneally, 2015). Victims often displayed symptoms of clinical

depression and anxiety disorders, which all have a high level of heritability (Mai-Duc,

2015). Therefore, we may be able to predict which children will demonstrate certain

cognitive and emotional-social endophenotypes (i.e., elevated psychopathological

markers without a clinical severity) that leaves them susceptible to becoming victims of

bullying (Gottesman & Gould, 2003). However, this effort must be accompanied with

prevention and intervention programs. Currently, even if victims had displayed symptoms

of emotional and cognitive deficits and had been diagnosed earlier with one of the

developmental psychopathological disorders prior to a tragic shooting event, there is yet

no program that exists to prevent bullying victimization of children with such disorders.

Developmental Psychopathology Endophenotype

Prevalence rates of bullying are higher among children with behavioral and

emotional difficulties. For instance, children with OCS disorders including ASD and

OCD report rates ranging from 46% to 80% in comparison to 30% among typically

developing children (Sterzing, et al., 2012; Cappadocia et al., 2012). Since both, OCD

and ASD, demonstrate deficits in visual attention, cognition of social cues (Baron-Cohen

11

et al., 1985; Van Roekel et al. 2010; Newton et al., 2017; Sterzing et al., 2012), and top-

down inhibitory control (Ciesielski et al., 1997; 2007; Greenberg et al., 2000; Loth,

Gomez & Happe, 2010), the investigation of a marker relying on top-down inhibitory

control is important for identifying children at risk for becoming victims of bullying.

Children with OCS disorders have marked severe social and communication

deficits and display repetitive behaviors (Bejerot, 2007). ASD and OCD share

commonalities in neurological and genetic phenomena and both have visual-spatial

processing deficits, impaired social interactions and communication, have restricted

interests in group activities and deficits related to ToM (Ivarsson & Melin 2008; Chasson

et al., 2011; Zandt et al., 2007; Bejerot, 2007; Anholt et al., 2009). Only recently has

research begun to examine the link between ASD and OCD to identify risk factors for

peer victimization.

Bejerot and Mörtberg (2009) examined autistic traits and previous experiences of

victimization during grade-school years on adult patients with social phobia, OCD and

healthy controls. The authors predicted that autistic traits would increase the likelihood of

being bullied even among those with social phobia and OCD. They found that patients

with OCD had higher rates of autistic traits compared to those with social phobia and

healthy controls. This group also reported higher rates of victimization (50%) compared

to patients with social phobia (20%) and healthy controls (27%). Thus, having autistic

traits increased the likelihood for becoming a victim among patients with OCD.

Children with ASD who reported higher levels of anxiety, hyperactivity, self-

injurious, and stereotypic behaviors were more likely to experience higher levels of

victimization (Cappadocia et al., 2012). These children were five times more likely to

12

have major communication difficulties. Victims were found to be much younger than

bullies, have fewer friends at school and were 11 times more likely to have higher levels

of child internalizing disorders (e.g., depression, anxiety).

Developmental Inhibitory Neuro-Endophenotype

Persistent poor inhibitory regulation of emotional and cognitive behavior may

increase the risk for becoming a target of bullying. Behavioral evidence demonstrates that

VC display deficits in top-down inhibitory control of both cognitive and emotional

responses. Studies have found that girls who had difficulty regulating their anger

responses were at higher risk for victimization. Boys who poorly suppressed their sadness

and worry reported higher levels of victimization (Alsaker & Gutzwiller-Helenfinger,

2010). Additionally, children in kindergarten who showed deficits in top-down inhibition

of distractions were at a higher risk for victimization (Alsaker & Nägele, 2008; Alsaker

& Gutzwiller-Helenfinger, 2010).

Evidence suggests that proficiency for top-down attentional modulation at early

stages of sensory processing continues to change during childhood and early adolescence

(Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Taylor, Chevalier, &

Lobaugh, 2003). Similar findings have been reported in studies on top-down attentional

control in children, as reflected in EEG oscillatory activity (Klimesch et al., 2001;

Krause, Salminen, Sillanmaki, & Holopainen, 2001). Parietal-occipital alpha was

reported to be mature in children ages 10 to 12 years (Krause et al., 2001; Krause,

Pesonen, & Hämäläinen, 2010) and found to be a sensitive indicator of cognitive

inhibitory task demands (Gevins, Smith, McEvoy, & Yu, 1997; Nunez, Wingeier, &

13

Silberstein, 2001). In our earlier study (Ciesielski et al., 2010), we used

magnetoencephalography (MEG) in 10-year-old children to examine developmental

differences in top-down control reflected in modulation of alpha-band (8–13 Hz) to

different stages of a working memory task with high demand for inhibitory control.

A recent study suggested, that focusing only on single-peak effects or one

frequency band may lead to an incomplete understanding of the mechanisms involved in

signal processing in stop-response tasks (Huster et al., 2014). The authors suggested that

a closer view of cortical activation to stop signal processing may be attained by using the

analysis of connectivity patterns and their dynamic changes over time or spatio-temporal

analysis of spectral oscillatory activity. The authors conclude that the critical time for

observation of effects of inhibitory brain action is prior to 200ms post stop-stimulus

presentation.

Stop-response task paradigms have been applied effectively in studies of

inhibitory control development. The characteristic findings report a sharp reduction in

reaction time of responses between childhood and adulthood (van den Wildenberg & van

der Molen, 2004). Developmental studies have found involvement of the frontal-basal

ganglia networks in response inhibition, both in adults and in children, and corresponding

activation in the frontal and parietal cortex (Cohen et al., 2010). Most developmental

neuroimaging studies use fMRI and mostly focus on the prefrontal cortical component of

the frontal-basal ganglia network. These have reported both increases (Ordaz et al., 2013)

and decreases (Durston et al., 2002) in prefrontal ventral activation related to inhibiting a

response. The contrast, increasing with age, between higher activity in control regions

relevant to inhibition and reduction of activity to non-specific regions has also been

14

reported (Ciesielski et al., 2006; Crone & Dahl, 2012; Durston & Casey, 2006; Luna et

al., 2010; Stephen et al., 2006).

Summary

Deficits in inhibitory control are well associated with phenomenology of

psychopathological disorders such as ASD or OCD. Early identification of such visual

inhibitory cognitive deficits, may point to risks for becoming a target of bullying

victimization. We aimed to examine whether abnormal top-down inhibitory control of

cognitive and emotional responses and ERPs, their neurophysiological correlates, may be

the elucidating fundamental neurological characteristic of all children victims and, thus, a

biological marker of children at high risk for becoming a victim of bullying. Defining a

reliable neural correlate of cognitive inhibitory control will be important for studying

developmental changes in the brain mechanism underlying adaptive behavior in the

challenging conditions of bullying victimization (Badre, Kayser & D'Esposito, 2010).

15

Aims & Hypotheses

To understand the mechanism of causes and effects of bullying in VC we aimed:

AIM 1: To characterize in VC, as compared to CC, the Trait Signatures

considered in developmental neuroscience to reflect permanent, pre-existing cognitive

and neural functions underlying top-down inhibitory control of behavior. Thus,

considering the clinical and research reports we expected in VC as compared to CC:

H1: Preserved functioning in earlier learned skills (e.g., verbal knowledge) less

susceptible to adverse events, but reduced performance in visual-spatial attention and

memory linked to symptoms of anxiety;

H2: Increased anxiety measures (e.g., CY-BOCS) and a history of poor social

interactions;

H3: Consistent reduction in alpha power and its task-related modulation, particularly in

the frontal and parietal brain regions.

AIM 2: To characterize State Signatures (SS) in VC, as compared to CC by

examining the cognitive, emotional and social effects of exposure to bullying.

Considering the aforementioned studies, we predicted in VC:

H1: Higher levels of depressive symptomology and traumatic experiences;

H2: Significant deficits in visuospatial tasks with high demands for organization and in

verbal and visual memory tasks;

H3: Poor, ineffective and mostly negative peer interactions, most likely related to

cognitive and emotional deficits.

AIM 3: The end goal was to identify risk factors for translational outcomes of this

study to facilitate programs of resilience in children vulnerable to victimization.

16

METHODOLOGY

Participants

A total of 46 children were recruited from the community (81.3%) and others

were invited from a longitudinal, developmental study (18.8%). In total, 17 children-

victim of bullying (VC) and 17 age and sex matched control children (CC) participated in

the study. The parents of the remaining 12 children recruited found it too time consuming

to complete the testing. All participants and their parents signed informed consent and

assent forms according to the guidelines of the Office of the Institutional Review Board

at the University of New Mexico. Inclusion criteria for VC included repeated experiences

with being a target of bullying victimization. Children in both groups were between age

6 to 17 years. Exclusionary criteria for CC included history or current clinical diagnosis

of any DSM-5 disorder and any history of being a bully or a victim of bullying. History

of CNS medication, traumatic brain injury, medical conditions (e.g., seizures,

tuberculous, genetic disorders) were exclusionary.

Measurements/Tasks

Cluster 1. Trait Signatures: Neurobehavioral and EEG Measurements

Consistent with our predictions that anxious children with deficient inhibitory

control would be in the high-risk group for bullying victimization, we clustered the

psychometric tools that reflect these characteristics into CLUSTER 1, the Trait

Signatures cluster. The tests included in this cluster were:

Wechsler Abbreviated Scale of Intelligence (Second Edition; Wechsler, 2011) –

Vocabulary (WASI-V). WASI-V was used to assess well learned and crystalized

17

abilities. It measures the participants knowledge of words and verbal concepts. The

subtest includes three-picture items and 38 verbal items. Participants were asked to define

words presented visually and orally.

Rey-Osterrieth Complex Figure Test (Rey, 1941|1993; Osterrieth, 1944|1993) –

Copy (CFT-C). CFT-C trial was used to assess visual-spatial attention, perception and

organization. Participants were instructed to copy a complex visual construct, which

requires low verbal mediation.

NIH Toolbox - Flanker Inhibitory Control and Attention Test (Hodes, Insel, &

Landis, 2013; NIH-FICA). NIH-FICA was used to measures attention and executive

function. The participant was asked to focus upon an arrow located in the middle of the

screen while inhibiting attention to stimuli nearby (i.e., other arrows). Participants

responded by pressing buttons to congruent and incongruent signals (i.e., 20 mixed

trials). There were four practice trials with audio recording feedback.

Child Yale–Brown Obsessive-Compulsive Scale (Scahill et al., 1997; CY-BOCS).

CY-BOCS was used to measure the severity of obsessive and compulsive symptoms. The

scale was comprised of a symptom checklist, target symptom list and severity rating on

10-items. The scale was administered to parents only.

The Social Communication Questionnaire (Rutter, Bailey & Lord, 2003; SCQ).

SCQ was used to measure ASD symptomatology. The screening questionnaire provided

levels of severity of ASD symptoms across different samples of behavior. Sub-scores

provided information on abnormal social interactions, communication and restricted,

repetitive and stereotyped patterns of behavior. The scale was administered to parents

only.

18

Structured Clinical Interview Parents provided information and records of

demographic data, prenatal and postnatal early development, and medical, personal and

family history. They also provided history of peer and family social interactions for their

child.

The normalized CFT-C and NIH-FICA scores were combined to provide a

composite score i.e., Visual Attention Index (VA-I). Scores indicate the standard

deviations above or below the mean. Responses describing past peer interaction from the

structural clinical interview and SCQ scores were combined to provide a Social

Interactions Index (SI-I) for both VC and CC. Higher scores indicated higher levels of

social interaction difficulties.

Blue Man-Stop Response Task (BM-SRT Task; Ciesielski, 2003; Bouchard et al.,

2018; presented in Figure 1). BM-SRT is a visual-spatial n-back working memory task

with a stop-response signal. It was designed in the form of a computer game for children.

Participants were instructed to concentrate on a fixation point and carefully observe the

Blue Men characters (angle size 2°) that were individually presented on the screen (each

for 200 ms). When the stop-response signal (i.e., blue soccer ball) was presented, the

participants were asked to decide if the two consecutive characters preceding the stop-

response signal were congruent or non-congruent in visual-spatial orientation.

Participants were permitted to respond when presented with the stop-release-signal (i.e.,

green dot). There were four awaiting-for-response periods between the soccer ball and

green dot: 1000ms, 1600ms, 3200ms and 4200ms.

The experiment was presented in six separate runs, lasting about three minutes

each, all consisting of 20 to 22 trials, for a total of 126 trials. All participants practiced

19

the task in a separate room before being prepared for the EEG to ensure familiarization.

They also completed a short practice run (i.e.,12 trials) in the EEG chamber, for which

the data were not recorded.

time

Figure 1. Schematic of Blue Man-Stop Response Task (BM-SRT). BM-SRT is a visual-spatial n-back working memory task with a stop-response signal. The task was to carefully observe individually presented Blue Men characters. When the stop-response signal (i.e., blue soccer ball) is presented, participants must decide if the last two consecutive characters were congruent or non-congruent in visual-spatial direction and respond when presented with the stop-release signal (i.e., green dot). The correct response for the sample trial above is congruent (Ciesielski, 2003; Bouchard et al., 2018).

Electrophysiological Data Acquisition.

Data for BM-SRT was collected with a BioSemi High Density EEG 64-channel

recording system (BioSemi B.V., Amsterdam, Netherlands) in a dimly lit room that was

noise and electromagnetic field shielded. EEG was recorded at a sampling rate of 512 Hz

and pass filtered online with a frequency of 0.16-100 Hz. The 64 channels were placed

Response Delay 1000, 1600, 3200, or 4200ms

Response Button Press

Stop-Response Signal

Stop-Release Signal

20

according to the international 10-20 system using the BioSemi Active2 system. Vertical

and horizontal eye movements were recorded using four electrooculography (EOG)

electrodes, two of which were placed on the outer eye canthi and two below and above

the left eye. Two additional reference electrodes were placed on the left and right

mastoids. All electrode locations were digitized using Polhemus. The experiment was

conducted using the Presentation software (Version 11.3) and responses were collected

using Current Designs 4 button Inline USB response box.

Cluster 2. State Signatures: Neurobehavioral Measurements

Consistent with the reported literature and our own hypotheses about the

depressive traits and related deficits in visual attention and memory, the psychometric

measurements expected to reveal the immediate consequences of victimization of

bullying were clustered into a battery of tasks named CLUSTER 2. These elucidated the

State Signatures of VC, which included:

Rey-Osterrieth Complex Figure Test (Rey, 1941|1993; Osterrieth, 1944|1993) –

Delayed Recall (CFT-DR). CFT-DR trial was used to assess visual-spatial perception,

attention, memory and organization. After participants were instructed to copy an

abstract image, they were asked to draw the image from memory after a 30-minute delay.

Wechsler Abbreviated Scale of Intelligence (Second Edition; Wechsler, 2011) –

Block Design (WASI-BD). WASI-BD was used to assess visual perception and

organization, nonverbal concept formation, and visual motor coordination. Participants

were asked to recreate two-dimensional designs from patterns in stimulus booklet using

21

blocks with red and white coloring. The test was time-limited, with extra-credit points

for fast rate of performance.

Wechsler Abbreviated Scale of Intelligence (Second Edition; Wechsler, 2011) –

Matrix Reasoning (WASI-MR). WASI-MR was used to assess fluid intelligence, visual-

spatial skills, and perceptual organization. The participants were asked to select response

options that complete a matrix or a sequence of stimuli.

Wechsler Abbreviated Scale of Intelligence (Second Edition; Wechsler, 2011) –

Similarities (WASI-S). WASI-S was used to assess verbal concept formation and

reasoning. Participants were given two words that represent similar objects or concepts

and they had to describe in their own words how these two objects were similar to each

other.

Benton Oral Word Fluency Test - FAS (Benton & Hamsher, 1976; FAS). FAS

was used to assess phonemic verbal fluency. Participants were instructed to say as many

words as possible within a minute, when given the letters F, A, or S; excluding proper

names, changing endings, or slang.

Children’s Depression Rating Scale-Revised (Poznanski & Mokros, 1996; CDRS-

R). CDRS-R was used to measure depression and severity of symptoms. The

questionnaire (parent version) was a semi structured interview that provided a cumulative

score for interpretation of scores representing behavioral and mood characteristics of a

child.

Questionnaire on Traumatic Experiences in Early Childhood (Tretiak &

Ciesielski, 2014; QTE). QTE was used to assess the participant’s history of exposure to

22

potentially traumatic life experiences. The 10-item questionnaire assessed direct exposure

to accidents or violence before the age of one, three, five, and ten.

Illinois Bully Scale (Espelage & Holt, 2001; IBS). IBS measured the frequency of

bullying, fighting and victimization by peers in the last 30 days. The psychometrics are

high for all subscales (i.e., bullying= 0.87; fighting= 0.83; victimization= 0.88). The

same questions were used to inquire about the participant’s history of bullying

victimization.

To control for multiple statistical comparisons, the normalized CFT-DR, WASI-

BD, WASI-MR scores were combined into a composite score named Visuo-Spatial Index

(VS-I). A relationship to normative score is provided in number of standard deviations.

The Verbal Expression Index (VE-I) included normalized WASI-S and FAS scores. The

scores obtained from IBS were combined from the assessment of history of bullying

victimization which provided an overall Bullying Victimization Severity Index (BVS-I) for

VC.

Data Analysis

EEG. Data were processed using EEGLAB toolbox for MATLAB (Delorme &

Makeig, 2004), which is an open source toolbox available for EEG signal processing.

Data was re-referenced to the average of the signal from the two mastoid electrodes.

Channel data from frontal and posterior regions were analyzed, which included left

frontal (LF; i.e., F1, F3, FC1, FC3), right frontal (RF; i.e., F2, F4, FC2, FC4), left parietal

(LP; i.e., P1, P3, PO3, O1), and right parietal (RP; i.e., P2, P4, PO4, O2) regions.

23

Only correct and congruent responses during the latency period (i.e., “waiting-to-

respond” window) in the BM-SRT were analyzed. The first 150ms of the first 1000ms

were removed and 150ms to 450ms after the stop-signal were averaged for analysis from

all four waiting latency periods (i.e., 1000ms, 1600ms, 3200ms, 4200ms). A high pass

filter of 0.01 Hz to 0.20 Hz was applied. Noisy channels were removed, and the data were

re-calculated to the average reference. Data were segmented into 2400ms epochs (200ms

pre-stimulus to 1000ms post-stimulus) and baseline corrected to the mean pre-stimulus

baseline. Interference signals resulting from gross body movements were removed from

the continuous data. An independent component analysis (ICA) was then conducted to

remove eye blinks and saccades. Spatial topography maps of the ICA components were

manually inspected to identify physiological activity and noisy components were

removed (Onton, 2010).

For each participant, the average alpha spectral power was calculated across the

four regions (i.e., LF, RF, RP, LP) using a custom written MATLAB script (The

MathWorks, Inc., Natick, MA). The spectral power was calculated using the decibel (dB)

conversion, which is the ratio between the strength of one signal (i.e., alpha frequency

band power) and the strength of another signal (i.e., baseline power of the same

frequency band; Cohen, 2014).

Statistical Analysis

Neurobehavior. Statistical data analysis was conducted using IBM SPSS version

24 and R version 3.4.3. Descriptive and frequency statistics were used to test for any

significant relationship between groups. Two-tailed independent t-tests were conducted

24

to determine statistical significant differences between VC and CC on the

neurobehavioral indices and behavioral reports of symptomology. False discovery rate

(FDR) was calculated to control for multiple comparisons using the Benjamini-Hochberg

(BH) method (Benjamini-Hochberg, 1995), when appropriate. Cohen’s d was calculated

to determine effect size, which was interpreted as .2 for small, .5 medium and .8 or

greater as large effects sizes (Cohen, 1988). Pearson’s r two-tailed correlations were

conducted for within-group analysis.

25

RESULTS

Rationale

The goal of this study was to characterize Trait Signatures (TS) and State

Signatures (SS) displayed by VC in contrast to CC. TS were assessed as representing the

long-term, some permanent and most likely pre-existing characteristics of children (e.g.,

demographics, prenatal and early postnatal development of language and social

interactions) that are vulnerable to becoming a target of bullying. SS, in contrast, were

assessed to identify the cumulative detrimental effects of bullying victimization on

cognitive, emotional and social functioning, as predicted by earlier studies. Our far-

reaching goal is to investigate whether children with a specific pattern of TS may form a

population that is at high-risk to becoming victims of bullying and translate these results

to models of early identification and thus, offering a preventive intervention.

Presented below are three sets of data: i.) demographics of both tested

populations, as a potential variable that may contribute to our understanding of a

differences in pattern of TS in VC population; ii.) TS measures, including scores on

anxiety tests, and pattern of alpha oscillatory activation during inhibitory control of

behavioral response, that were considered a long-term physiological indicator of top-

down inhibition; and iii.) SS data, that involved measures of consequences of exposure to

bullying in the emotional, social and cognitive domain.

Demographics

A total of 34 children (17 VC) completed all study tests and procedures. One

participant from each group were excluded due to their difficulty to follow task rules or

26

disclosure of exclusionary criteria during study participation. A total of 16 VC and 16 CC

were included in the final analyses. Participant characteristics are described in Table 1.

Majority of the participants in the VC group identified as Hispanic (56.3%), while

CC generally identified as non-Hispanic, White (43.8%). The majority of VC were in

middle school (37.4%), while CC in elementary school (56.3%). There were no

significant group differences observed in race/ethnicity identification or SES as reported

in Table 1. The groups did not differ in terms of Verbal Comprehension Index (VCI; t24.7

= -1.53, p = .14) or the Perceptual Reasoning Index (PRI; t30 = -1.74, p = .09), calculated

from WASI-II.

27

Table 1.

Participant Demographics

Children-Victim (n=16) Control Children (n=16) pa

Characteristic n % M SD n % M SD

Age 12.5 3.01 10.4 3.22 .064

SESb 46.3 11.3 47.2 10.9 .81

WASI-VCI 102 8.7 108 12.4 .14

WASI-PRI 96.9 9.4 104 14.4 .09

Sex .73

Female 9 56 8 50

Male 7 44 8 50

Race/Ethnicity .053

American Indian 0 0 1 6.3

Asian 1 6.3 0 0

Bi-racial 3 18.8 0 0

Black/African American 1 6.3 0 0

Hispanic 9 56.3 8 50

Non-Hispanic White 2 12.5 7 43.8

School Grade .30

Elementary School 5 31.3 9 56.3

Middle School 6 37.4 3 28.7

High School or higher 5 31.3 4 25

Note. No group differences in demographic characteristic between Children-Victim and Control Children.

WASI-VCI=WASI-II Verbal Comprehension Index; WASI-PRI=WASI-II Perceptual Reasoning Index aSignifiance was evaluated by independent sample t-tests. bAccording to Hollingshead socioeconomic status

codes (Hollingshead, 1975).

28

Trait Signatures

Between-Group Analysis

Neurobehavioral Measurements. Independent t-tests were conducted to determine

statistical significant differences between VC and CC on the WASI-Vocabulary (WASI-

V) and Visual Attention Index (VA-I). FDR correction was employed to control for

multiple comparisons and the adjusted p-value was estimated using the BH method

(hereinafter, only adjusted pBH-values are reported when appropriate). There was a

statistically significant group difference in performances between groups in WASI-V and

VA-I (as reported in Appendix A).

Independent t-tests were used to assess group differences in the Social Interaction

Index (SI-I), CY-BOCS and performance of correct responses on BM-SRT. Significant

group differences were observed in the SI-I (pBH = .018), with VC reporting higher rates

of poor peer relationships, evidenced even in their early school years. CY-BOCS was not

statistically different between VC and CC, however, a medium effect size was observed

in VC. Due to high levels of noise during BM-SRT, one VC and two CC, were removed

from all related analysis, including correct responses. No significant differences were

observed for correct responses in the BM-SRT across both groups (t27 = 1.01, p = .32, d =

.39).

29

Figure 2. Contrasts in Trait Signatures between Children-Victim (VC) and Control Children (CC). There was a significant difference in WASI-V, VA-I, and SI-I between VC and CC, even after adjusting for multiple corrections using the False Discovery Rate. Numerical findings are in Appendix A. WASI-V=WASI-II Vocabulary; VA-I=Visual Attention Index; SI-I=Social Interactions Index; CY-BOCS=Child Yale–Brown Obsessive-Compulsive Scale. Alpha Amplitude for BM-SRT.

The alpha power was assessed during the delay period while awaiting to respond

across four cortical regions (i.e., LF, RF, LP, RP). Independent t-tests were conducted to

test for statistical significant differences between VC and CC spectral alpha power. As

indicated before, one VC participant and two CC participants were removed from further

data processing due to high level of motion noise in data. No significant group

differences in alpha power were observed in any brain region as seen in Table 2.

30

Table 2.

Trait Signatures: Alpha Amplitude for BM-SRT

VC CC t df pa d 95% CI of d

Region M SD M SD LL UL

Left Frontal 0.18 0.24 0.22 0.26 -0.44 27 .67 -0.17 -0.25 0.08

Right Frontal 0.44 0.41 0.51 0.53 -0.44 27 .67 -0.15 -0.32 0.01

Left Parietal 0.56 0.68 0.57 0.85 -0.04 27 .97 -0.01 -0.28 0.26

Right Parietal 0.67 1.00 0.65 1.10 0.07 27 .95 0.02 -0.35 0.39

Note. Significance was evaluated by independent sample t-tests. d = Cohen’s d effect size; CI=Confidence

Interval; LL=Lower level; UL=Upper Level. aUncorrected for multiple comparisons p value

Within-Group Analysis

EEG. Pearson’s r correlations revealed strong power coherence between frontal

and parietal regions in both VC and CC, as indicated in Figure 3. A positive statistically

significant correlation was found between the LP and RP in both VC (Pearson’s r(15) =

.98, pBH < .001) and CC (Pearson’s r(14) = .96, pBH < .001) during the BM-SRT. Further,

there were significant correlations between higher percent of correct responses in BM-

SRT and high alpha power in LP, RF and LF (pBH = .044, pBH = .044, & pBH = .049,

respectively) regions in CC only.

31

Figure 3. Alpha Power Correlograms. Pearson r values are respresented in the correlograms for a) Vicitmized Children (VC) and b) Control Children (CC) between frontal-parietal areas during BMT-SRT awaiting-to-respond period. The size of the circle indicate the strength of correlation (e.g., bigger circle, stronger relationship), while the color represents the direction of relationship (e.g., positive or negative correlation). Both correlograms demonstrante a positive correlation in frontal-parietal regions for VC and CC. Numerical findings are in Appendix B.

Neurobehavioral. Pearson’s r correlations were conducted to determine if alpha

power was correlated with family history of anxious and OCS disorders and severity of

victimization. As seen in Table 3, no significant findings were found, however, there

were medium effect sizes observed in alpha power and history of DSM-5 disorders in

VC. CC did not have signficant findings as seen in Appendix C.

32

Table 3.

Trait Signatures: Neurobehavioral Correlations in Children-Victim

Variables H-DSM BVS-I LF RF LP RP

H-DSM - .20 -.42 -.42 -.38 -.35

BSI-I - -.02 -.09 -.11 -.10 Note. Pearson’s r values presented. H-DSM=History of DSM-5 Disorders; BVS-

I=Bullying Victimivation Severity Index; LF=Left Frontal; RF=Right Frontal; LP=Left

parietal; RP=Right Parietal.

Conclusions: Trait Signatures displayed by VC as compared to CC involved:

(i) Significant deficits in basic cognitive and visual attention abilities;

(ii) Higher levels of anxiety characteristics prior to victimization (e.g.,

displayed more prevalent OCS phenomena);

(iii) No significant group differences in alpha power were recorded during top-

down inhibition of motor act and the correlations between alpha power in

frontal-parietal network were not statistically significant between groups

either.

33

State Signatures

Based on the participant’s reporting, verbal victimization was the most prevalent

(88%), such as being called derogative names or receiving demeaning commands. Other

frequently experienced bullying acts, were social victimization expressed in exclusions

and ridiculing in front of others (19%) and physical victimization including hitting and

pushing (19%). Further, 25% of VC reported having experienced all three forms of

bullying victimization. On average, 63% of VC had been bullied multiple times within

the last 30 days as reported by the Illinois Bully Scale (Espelage & Holt, 2001). The

scores on this scale suggested that severity of bullying victimization phenomena was

within a moderate range.

Between-Group Analysis

Neurobehavioral Measurements. Independent t-tests were conducted to determine

statistical significant differences between VC and CC on the Visuo-Spatial Index (VS-I)

and Verbal Expression Index (VE-I). There were no statistically significant group effects

of bullying victimization on either measure. However, there was large effect size

observed for VS-I according to Cohen’s d effect size (Cohen, 1988).

Independent t-tests, were used to assess group differences in SS of emotional and

social functioning. Significant group differences were observed in CDRS-R (t20.33 = 3.88,

pBH = 0.002, d = 1.47), showing higher depression symptoms in VC relative to CC. There

was a significant difference in the QTE (t24.65 = 2.08, pBH = 0.048, d = 0.76) and BVS-I

(t30 = 6.22, pBH < 0.001, d = 2.22), which indicated VC reported higher number of

34

traumatic experiences and bullying victimization severity than CC. Further, large effect

sizes were observed across all three measures as seen in Appendix D.

Figure 4. Contrasts in Neurocognitive State Signatures between Children-Victim (VC) and Control Children (CC). There was a significant difference in VS-I between VC and CC but was no longer significant after adjusting for multiple comparisons. There was no significant difference found in VE-I between VC and CC. Numerical findings are in Appendix D. VS-I=Visuo-Spatial Index; VE-I=Verbal Expression Index.

35

Figure 5. Contrasts in Clinical State Signatures between Children-Victim (VC) and Control Children (CC). There was a significant difference in CDRS-R and QTE between VC and CC, even after adjusting for multiple corrections using the False Discovery Rate. Numerical findings are in Appendix D. CDRS-R=Children’s Depression Rating Scale-Revised; QTE=Questionnaire on Traumatic Experiences in Early Childhood.

Within-Group Analysis

Neurobehavioral. Pearson’s r correlations were conducted to examine if severity

of cognitive and emotional deficits was correlated with current state of peer victimization

(SS-PV) in VC. No statistically significant correlations were found between VS-I (pBH =

.71), VE-I (pBH = .20), and QTE (pBH = .58). However, there was a significant

relationship in the CDRS-R (r = .68, pBH = .032), indicating higher scores in depression

36

symptomology and severe negative peer interactions. There was a medium effect size

between SS-PV and VE-I (r = -.44) as reported in Table 4 (Cohen, 1988).

Table 4.

State Signatures: Neurobehavioral Correlations in Children-Victim

Variable SS-PV VE-I VS-I CDRS-R QTE

SS-PV - -.44 -.11 .68* .22

Note. Pearson’s r values presented. Multiple comparisons controlled using False Discovery rate. SS-

PV=State Signatures Peer Victimization; CDRS-R=Children’s Depression Rating Scale-Revised;

QTE=Questionnaire on Traumatic Experiences in Early Childhood; VS-I=Visuo-Spatial Index; VE-

I=Verbal Expression Index. *p < .05

Conclusions: Our key findings in State Signatures for VC as compared to CC were:

(i) Significantly higher levels of depressive moods;

(ii) Significant deficits in visuospatial memory and visually mediated

conceptualization;

(iii) Ongoing traumatic experiences including bullying victimization.

37

DISCUSSION

The central aim of the present study was to characterize children-victim (VC) by

peer bullying victimization on two dimensions, Trait Signatures precipitating the abuse,

and State Signatures, the long-term consequences of persistent abuse. The

neurobehavioral and electrophysiological assessment of VC using a battery of tests

representing Trait Signatures showed, in VC as compared to CC, significantly higher

levels of anxiety including social situations, in line with lower visual-spatial attentional

skills and attention to visual details. VC also showed higher compulsivity as reported by

CY-BOCS. This may indicate lower top-down inhibitory control which may further

hinder their social interactions. It is of interest, however, that in this context the event-

related potentials data did not reveal significant group differences in power of alpha

oscillatory activity within the frontal-parietal network, implicated in top-down inhibitory

regulation of behavior (Bressler et al., 2008).

Alpha power is the earliest maturing oscillatory rhythm in the central nervous

system, which becomes relatively stable after the age of 5. This make it an ideal

developmental factor to study as a TS. The above result of no group differences may

result from suppression of alpha relative to baseline as compared to CC. Additionally, we

found large effect sizes in the relationship between alpha power in the left frontal and

right frontal regions in the first-degree familial history of psychiatric disorders in the VC

group. Alpha power in these regions was negatively correlated with familial psychiatric

disorders.

38

The no-group differences in alpha may suggest two additional possible

explanations, considering that there were no group differences on the BM-SRT

performance. The task we were employing to gather EEG spectral oscillatory brain

activity was not specific to the population of VC and relied mostly on controlling a pure

motor response. Including an emotionally charged stimuli in future studies would be

more specific to VC high anxiety characteristics. The second explanation may consider

the severity of anxiety, that in this study was within a mild-to-moderate level, and its

impact on a child’s behavior may be modest, but, yet sufficient to make VC more

susceptible to becoming a target of bullying. Participants in this study represent a healthy

child population with verbal general intellectual abilities and perceptual intellectual

abilities showing no significant group differences and remaining within average-to-high

average intellectual level. The cognitive task we used in the EEG laboratory might have

been relatively easy to take. However, in a socially challenging interaction with their peer

group, these children’s anxiety and poor visual-spatial attention to details may be a

challenge in responding fast and appropriately to the aggressive behavior of others.

Future investigations of the mechanism of bullying in young children must include an

emotionally charged stimuli and include children with higher levels of severity of

victimization.

The description of a child vulnerable to victimization by bullying is

complemented by our findings of State Signatures. VC, as compared to CC, showed

significantly higher levels of depressive moods and, consistent with the depressive

symptoms, increased difficulties in visual-spatial memory and conceptualization. The

scale levels for assessing past and current traumatic experiences including bullying

39

victimization, was significantly higher in VC. Thus, the SS records display a child with

significant depression that adds to primary existing anxious states. The past experience of

traumatic events, and current low social abilities demonstrate a child at high risk for

social victimization.

Of interest, are our findings on consistent visual-spatial attention and memory

problems in the VC group. This abnormality in visuospatial processing is similar to

children who have been maltreated and display symptoms of Posttraumatic Stress

Disorder (PTSD; De Bellis, Woolley & Hooper, 2013; Vasilevski & Tucker, 2016;

Barrera-Valencia, Calderon-Delgado, Trejos-Castillo, & O’Boyle, 2017). Further, both

emotional processing and visuospatial processing are associated with right hemisphere

functioning (Liotti & Mayberg, 2001), which has been suggested to influence the ability

to properly attend to taxing emotional effort due to high levels of negative emotions.

There was no difference found in verbal functioning in either group. The FAS

subtest, included in the Verbal Expression Index (i.e., FAS, WASI-S), requires flexibility

but provides the ability for participants to choose their own wording. This indicates that

VC have adequate ability in their expressions when they are cognitive and not emotional,

which is related to relatively intact left hemisphere functions. In consistency, Beers and

De Bellis (2002) also found no difference in FAS and Similarities among children who

had maltreatment-related PTSD.

Parental reports of VC revealed higher levels of depression as compared to CC,

which is consistent with other studies focusing on consequences of bullying in children

(Nishina et al, 2005). Farrington and colleagues (2012) found that bullying victimization

was a significant predictor of depression up to seven years later after controlling for other

40

major childhood adverse traumatic events. Additionally, VC reported higher rates of

traumatic experiences in contrast to CC of other nature, not only victimization by

bullying. This is of significant concern since experiencing trauma can alter

developmental trajectory of a child’s emotional, behavioral, and cognitive growth as seen

with children who have been maltreated or abused. Sensitive periods during development

in early adolescence related to synaptic pruning and development of myelination is

essential to healthy development of networks (Fair et al., 2007; Schaefer et al., 2014;

Meng & Xiang, 2016; Stevens, 2016) and can be severely delayed due to trauma (Wilson,

Hansen, & Li, 2011). This delay of myelination can in turn lead to functional deficits in

attention, executive functions and visual-spatial processing.

Our data indicated that VC displayed TS deficits in verbal memory and

knowledge. This may leave them less effective in verbal communication and more

vulnerable to bullying. However, this finding may be biased due to cultural constraints of

the test such as the predominate mainstream culture acceptable responses. We posit this

shortcoming in vocabulary in children from minority cultures may have a significant

impact in initiation of everyday social communication and may represent actual social

communication in typical contexts. Parents often reported that VC were bullied because

of their poor verbal communication skills.

The TS and SS data are crucial for our understanding of the roots of child’s

vulnerability to becoming a victim of bullying and provide us with markers that we need

to focus on to when aiming for early identification of children at risk for bullying

victimization. Further, the results open a possibility to translate them into an effective

intervention program that could be developed very early before children enter the primary

41

school environment. These preventive interventions must address emotional anxiety

treatment, increase child resilience and assist in perception and interpretation of social

cues.

There are many other risk factors that we need to include in a successful design of

preventive programs, such as gender and SES. Although the racial and ethnic

composition of both groups in our study were diverse and representative of the

population in New Mexico, VC children had slightly higher rates of racial/ethnic

minority backgrounds compared to CC.

Study Limitations

A potential limitation in our study was the variable and relatively moderate levels

of severity and short length of exposure in tested children. This could underlie the modest

spectral power effect size seen during the BM-SRT. Increasing these effects may require

in future studies at least three levels of experienced bullying severity, a low to moderate,

severe and prolonged, and no exposure to bullying. Therefore, it is notable, that although

we tested children with only a mild-to-moderate severity of bullying exposure, children-

victim still revealed high levels of depression, slowness in fluency of verbal expression

and in attention to complex visual attention. Other limitations to our model of Trait and

State characteristics of Children-Victim include a small sample size and relatively broad

age-rage of tested children, that could introduce an extra developmental variance. These

properties of the design could only be addressed within a larger program of studies.

42

Future Studies

The current findings will form a foundation for future translational studies

investigating State and Trait Signatures of victimization by social bullying in

neuroimaging of cognition, psycho-motor coordination, as well as emotional and social

development.

Summary

In conclusion, the study aimed to characterize risk factors for bullying

victimization displayed in Trait Signatures, which were hypothesized to precipitate the

susceptibility to victimization and to State Signatures resulting from bullying. We found

that Children-Victim manifesting Trait Signatures, in visual attention and verbal

communication along with traits of anxiety and obsessive-compulsive phenomena,

although not manifesting differences in alpha power during top-down inhibition of motor

responses, were at a higher risk for bullying victimization. Among State Signatures of

Children-Victim, through rigorous testing of neuropsychological skills, significant

deficits were found in complex visuospatial perception, memory and organization,

dysfunctions that may contribute to poor interpretation of social cues. The ultimate-aim

of this study was to further facilitate efforts toward designing empirically based programs

of resilience against bullying for young children.

43

APPENDIX A

Trait Signatures: Neurobehavioral Measurements

Children-

Victim

Control

Children t df pa d 95% CI of d

Measure M SD M SD LL UL

WASI-V 0.044 0.74 0.61 0.70 -2.23 30 0.33* -0.81 -1.05 -0.57

VA-I -0.79 0.96 -0.12 0.50 -2.28 30 0.019* -0.9 -1.16 -0.65

SI-I 3.93 1.74 2.32 1.37 2.80 28 0.009* 1.06 0.51 1.63

CY-BOCS 1.19 1.56 0.38 0.72 1.89 21.1 0.072 0.69 0.28 1.10

Note. Significance was evaluated by independent sample t-tests. Multiple comparison controlled using

False Discovery Rate. d=Cohen’s d effect size; WASI-V=WASI-II Vocabulary; VA-I=Visual Attention

Index; SI-I=Social Interactions Index; CY-BOCS=Child Yale-Brown Obsessive-Compulsive Scale;

CI=Confidence Interval; LL=Lower level; UP=Upper Level. aUncorrected for multiple comparisons p value. *p = <.05.

44

APPENDIX B

Trait Signatures: Resting State Alpha Power

Regions LF RF LP RP

LF - .83** .71** .70**

RF .89** - .93** .90**

LP .85** .89** - .94**

RP .85** .83** .97** -

Note. Pearson’s r correlations presented for Children-Victim above the diagonal and

Control Children below the diagonal. LF=Left Frontal; RF=Right Frontal; LP=Left

Parietal; RP=Right Parietal. * p < .05 (2-tailed) after False Discovery Rate (FDR) correction; ** p < .01 (2-tailed) after

FDR correction.

45

APPENDIX C

Trait Signatures: Neurobehavioral Correlations in Control Children

Variables H-DSM BVS-I LF RF LP RP

H-DSM - .02 -.06 -.27 -.19 -.16

BSI-I - -.27 -.21 -.38 -.35

Note. Pearson’s r values presented. H-DSM=History of DSM-5 Disorders; BVS-

I=Bullying Victimivation Severity Index; LF=Left Frontal; RF=Right Frontal;

LP=Left Parietal; RP=Right Parietal.

46

APPENDIX D

State Signatures: Neurobehavioral Measurements

Children-

Victim

Control

Children t df pa d 95% CI of d

Measure M SD M SD LL UL

VS-I -0.46 0.54 -0.003 0.61 -2.27 30 0.031 -0.82 -1.01 -0.63

VE-I -0.11 0.85 -0.01 0.76 -0.36 30 0.72 -0.13 -0.40 0.14

CDRS-R 45.13 11.04 32.83 5.4 3.88 20.3 0.001** 1.47 -1.45 4.38

QTE 2.94 2.08 1.67 1.23 2.08 24.7 0.048** 0.77 0.19 1.34

BVS-I 2.06 0.36 1.22 0.41 6.22 30 < 0.001** 2.25 2.12 2.38

Note. Significance was evaluated by independent sample t-tests. Multiple comparison controlled using False

Discovery Rate. d=Cohen’s d effect size; VS-I=Visuo-Spatial Index; VE-I=Verbal Expression Index; CDRS-

R=Children’s Depression Rating Scale-Revised; QTE=Questionnaire on Traumatic Experiences in Early

Childhood; BVS-I=Bullying Victimization Severity Index; CI=Confidence Interval; LL=Lower level;

UL=Upper Level. aUncorrected for multiple comparisons p value. *p = < .05; **p = < .01.

47

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