Addressing Efficacy in a Clinical Setting

Evaluating 19-Channel Z-score Neurofeedback:

Addressing Efficacy in a Clinical Setting

Submitted by

Nancy L. Wigton

A Dissertation Presented in Partial Fulfillment

of the Requirements for the Degree

Doctorate of Philosophy

Grand Canyon University

Phoenix, Arizona

May 15, 2014

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© by Nancy L. Wigton, 2014

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Abstract

Neurofeedback (NF) is gaining recognition as an evidence-based intervention grounded

in learning theory, and 19-channel z-score neurofeedback (19ZNF) is a new NF model.

Peer-reviewed literature is lacking regarding empirical-based evaluation of 19ZNF. The

purpose of this quantitative research study was to evaluate the efficacy of 19ZNF, in a

clinical setting, using archival data from a Southwest NF practice, with a retrospective

one-group pretest-posttest design. Each of the outcome measures framed a group such

that 19ZNF was evaluated, as it relates to the particular neuropsychological constructs of

attention (n = 10), behavior (n = 14), executive function (n = 12), as well as

electrocortical functioning (n = 21). The research questions asked if 19ZNF improves

these constructs. One-tailed t tests performed, compared pre-post scores for included

clinical assessment scales, and selected quantitative electroencephalographic (QEEG)

metrics. For all pre-post comparisons, the direction of change was in the predicted

direction. Moreover, for all outcome measures, the group means were beyond the

clinically significant threshold before 19ZNF, and no longer clinically significant after

19ZNF. All differences were statistically significant, with results ranging from p = .000

to p = .008; and effect sizes ranging from 1.29 to 3.42. Results suggest 19ZNF improved

attention, behavior, executive function, and electrocortical function. This study provides

beginning evidence of 19ZNF’s efficacy, adds to what is known about 19ZNF, and offers

an innovative approach for using QEEG metrics as outcome measures. These results may

lead to a greater acceptance of 19ZNF, as well as foster needed additional scientific

research.

Keywords: Neurofeedback, QEEG, z-score neurofeedback, 19ZNF, EEG biofeedback

v

Dedication

This dissertation is dedicated to my Lord and Savior, Jesus. From my first

thoughts of considering a doctoral program being divinely inspired and directed, through

to the last step I will take across a graduation stage, the Father, Son, and Holy Spirit are

always the center point, the anchor. To that end, three Bible passages capture the

experience of my journey.

The way of God is perfect, the Lord’s word has stood the test; He is the shield of

all who take refuge in Him. What god is there but the Lord? What rock but our

God? – the God who girds me with strength and makes my way blameless, who

makes me swift as the deer and sets me secure on the mountains (Psalms 18:30-

33, New English Bible).

“Commit your life to the Lord; trust in Him and He will act. He will make your

righteousness shine clear as the day and the justice of your cause like the sun at noon”

(Psalms 37:5-6).

“Not to us, O Lord, not to us, but to thy name ascribe the glory, for thy true love

and for thy constancy” (Psalms 115:1).

vi

Acknowledgments

It is only through the Lord’s strength and wisdom that this dissertation came to

fruition. Next, I acknowledge the man with whom the Lord has made me one, my

husband. You are truly the wind beneath my wings, and without you I would not have

had the wherewithal to complete this endeavor. Thank you for all your support and

sharing your perseverance for my good. I also wish to acknowledge, with unbounded

gratitude, the most perfect dissertation committee possible for this journey.

To my chair, Dr. Genomary Krigbaum, words are insufficient to fully express the

depth and breadth of my appreciation for your support, guidance, and direction. When I

first read descriptions of what the ideal chair would be, with characteristics inclusive of

mentor, advocate, role model, teacher, defender, guide, supervisor, coach, encourager,

and friend, I wondered if it would ever be possible to find all those elements in one

person. Yet in you, I found them all, and more. Por siempre agradecida. Moreover, thank

you for encouraging me to build on the methodology you started. To Dr. Daniel Smith, I

am grateful that you joined my dissertation team. I knew I could count on you for your

statistical expertise, and you did not disappoint. Thank you for the many conversations

prior to my dissertation journey, and in helping to pave the way for the best committee

possible. To Dr. Genie Bodenhamer-Davis, as a most respected neurofeedback

practitioner and educator, I am humbled and honored that you were willing to assist me in

my dissertation journey. Thank you, so much, for your counsel over the last 3 years. To

Dr. Ron Bonnstetter, thank you for your support in being my adjunct dissertation reader.

Thank you for your compliments on my writing and your assurance I have what it takes

to succeed as a scholar.

vii

Table of Contents

List of Tables ……………………………………………………………………………………………………… xi

List of Figures ……………………………………………………………………………………………………. xii

Chapter 1: Introduction to the Study …………………………………………………………………………1

Introduction ……………………………………………………………………………………………………..1

Background of the Study …………………………………………………………………………………..2

Problem Statement ……………………………………………………………………………………………4

Purpose of the Study …………………………………………………………………………………………5

Research Questions and Hypotheses …………………………………………………………………..6

Advancing Scientific Knowledge ……………………………………………………………………….8

Significance of the Study …………………………………………………………………………………..9

Rationale for Methodology ………………………………………………………………………………10

Nature of the Research Design for the Study ………………………………………………………11

Definition of Terms…………………………………………………………………………………………13

Assumptions, Limitations, Delimitations …………………………………………………………..19

Summary and Organization of the Remainder of the Study ………………………………….22

Chapter 2: Literature Review …………………………………………………………………………………23

Introduction and Background to the Problem ……………………………………………………..23

Historical overview of EEG and QEEG. ……………………………………………….24

Historical overview of NF …………………………………………………………………..25

How problem/gap of 19ZNF research evolved into current form ……………..28

Theoretical Foundations and/or Conceptual Framework ………………………………………31

Foundations of EEG and QEEG …………………………………………………………..31

viii

Learning theory as applied to NF………………………………………………………….31

Traditional/amplitude-based models of NF ……………………………………………33

QNF model of NF ………………………………………………………………………………35

ZNF model of NF……………………………………………………………………………….38

Review of the Literature – Key Themes …………………………………………………………….39

QNF in the literature …………………………………………………………………………..39

4ZNF in the literature………………………………………………………………………….47

19ZNF in the literature………………………………………………………………………..50

Outcome measures for ZNF research ……………………………………………………53

Summary ……………………………………………………………………………………………………….59

Chapter 3: Methodology ……………………………………………………………………………………….61

Introduction ……………………………………………………………………………………………………61

Statement of the Problem …………………………………………………………………………………61

Research Questions and Hypotheses …………………………………………………………………62

Research Methodology ……………………………………………………………………………………64

Research Design……………………………………………………………………………………………..65

Population and Sample Selection………………………………………………………………………66

Instrumentation ………………………………………………………………………………………………68

Validity …………………………………………………………………………………………………………72

Reliability ………………………………………………………………………………………………………74

Data Collection Procedures ………………………………………………………………………………76

Data Analysis Procedures ………………………………………………………………………………..78

Ethical Considerations …………………………………………………………………………………….81

ix

Limitations …………………………………………………………………………………………………….82

Summary ……………………………………………………………………………………………………….84

Chapter 4: Data Analysis and Results ……………………………………………………………………..86

Introduction ……………………………………………………………………………………………………86

Descriptive Data……………………………………………………………………………………………..86

Data Analysis Procedures ………………………………………………………………………………..93

Results …………………………………………………………………………………………………………..96

Summary ……………………………………………………………………………………………………..103

Chapter 5: Summary, Conclusions, and Recommendations ……………………………………..105

Introduction ………………………………………………………………………………………………….105

Summary of the Study …………………………………………………………………………………..106

Summary of Findings and Conclusion ……………………………………………………………..107

Implications………………………………………………………………………………………………….113

Theoretical implications…………………………………………………………………….114

Practical implications ………………………………………………………………………..115

Future implications. ………………………………………………………………………….116

Recommendations …………………………………………………………………………………………117

Recommendations for future research. ………………………………………………..117

Recommendations for practice. ………………………………………………………….118

References …………………………………………………………………………………………………………120

Appendix A ……………………………………………………………………………………………………….136

Appendix B ……………………………………………………………………………………………………….137

x

Appendix C ……………………………………………………………………………………………………….138

Appendix D ……………………………………………………………………………………………………….139

xi

List of Tables

Table 1.1. Research Questions and Variables …………………………………………………………….8

Table 4.1. Descriptive Data for All Groups ……………………………………………………………. 91

Table 4.2. Shapiro-wilk Results for Difference Scores ……………………………………………. 95

Table 4.3. Summary of Results – All Groups………………………………………………………….104

xii

List of Figures

Figure 1.1. Formation of Sample Groups ………………………………………………………………. 13

Figure 4.1. IVA Group Pre-Post Scores…………………………………………………………………. 97

Figure 4.2. DSMD Group Pre-Post Scores …………………………………………………………….. 99

Figure 4.3. BRIEF Group Pre-Post Scores …………………………………………………………… 101

Figure 4.4. QEEG Group Pre-Post Scores …………………………………………………………… 102

1

Chapter 1: Introduction to the Study

Introduction

Neurofeedback (NF) is an operant conditioning brainwave biofeedback technique,

which is also referred to as electroencephalographic (EEG) biofeedback. This modality,

dating back to the 1970s (Lubar & Shouse, 1976; Sterman, LoPresti, & Fairchild, 2010),

trains electrical signals of targeted frequencies and involves recording EEG data from

scalp sensors with an amplifier, which is subsequently processed by computer software.

The software provides visual and sound display feedback to the trainee, thereby

providing a reward stimulus when the brain is functioning in the target range. This

reward process generates learning such that the brain’s functioning is conditioned in the

intended manner.

Over the years, new models of NF have been developed, and the most current

iteration is a style of NF which is termed z-score NF (ZNF). ZNF is different from more

traditional NF models in that it incorporates into the NF session real-time quantitative

EEG (QEEG) z-score metrics making it possible to combine operant conditioning with

real-time assessment using a normative database (Collura, Thatcher, Smith, Lambos, &

Stark 2009; Thatcher, 2012). In 2006, a 4-channel ZNF (4ZNF) technique was

introduced, which in 2009 was expanded to include all 19 sites of the International 10-20

System (of electrode placement) to allow for a 19-channel ZNF (19ZNF). To date, case

study and anecdotal clinical reports within the field indicate this new 19ZNF approach is

an improvement over traditional NF models (J. L. Koberda, Moses, Koberda & Koberda,

2012a; Wigton, 2013). However the efficacy of this new model has not yet been

established from empirical studies. This research is different from prior qualitative

2

studies; it has been completed as a quantitative analysis of pre-post outcome measures

with group data, and thus, it is a beginning in establishing empirical evidence regarding

19ZNF.

The remainder of this chapter formulates this dissertation through a review of the

study background, problem statement, purpose and significance, and how this research

advances the scientific knowledge. Moreover the research questions and hypotheses are

presented, together with the methodology rationale and the nature of the research design.

An extended Definition section is included to review the many technical terms germane

to this research. Readers unfamiliar with NF or QEEGs may find it helpful to review the

definitions first. Finally, to establish the scope of the study, a list of assumptions,

limitations, and delimitations are included.

Background of the Study

In recent years NF has seen increasing acceptance as a therapeutic technique.

Current literature includes reviews and meta-analyses which establish a recognition of

NF as effective for the specific condition of attention deficit hyperactivity disorder

(ADHD) (Arns, de Ridder, Strehl, Breteler, & Coenen 2009; Brandeis, 2011;

Gevensleben, Rothenberger, Moll, & Heinrich, 2012; Lofthouse, Arnold, Hersch, Hurt, &

DeBeus, 2012; Niv, 2013; Pigott, De Biase, Bodenhamer-Davis, & Davis, 2013).

However, the type of NF covered in these reviews is limited to the oldest NF model

(theta/beta ratio) and/or slow cortical potential NF. Yet of note are reports in the literature

of a different NF model which is informed by QEEG data. This QEEG-guided NF (QNF)

is reported to be used for a much wider range of conditions; not only ADHD, but also

behavior disorders, cognitive dysfunction, various mood disorders, epilepsy,

3

posttraumatic stress disorder, head injuries, autism spectrum disorders, migraines,

learning disorders, schizophrenia, and mental retardation (Arns, Drinkenburg, &

Kenemans, 2012; Breteler, Arns, Peters, Giepmans, & Verhoeven, 2010; Coben &

Myers, 2010; J. L. Koberda, Hillier, Jones, Moses, & Koberda 2012; Surmeli, Ertem,

Eralp, & Kos, 2012; Surmeli & Ertem, 2009, 2010, 2011; Walker, 2009, 2010b, 2011,

2012b).

Yet, all the aforementioned models are limited in their use of only one or two

electrodes and they also require many sessions to achieve good clinical outcomes. For the

above-cited studies the reported average number of sessions was 40.5. Moreover,

Thatcher (2012, 2013) reports 40 to 80 sessions to be the accepted norm for these older

style models; thus leading to a sizeable cost to access this treatment. However, one of the

newest ZNF models shows promise to bring about positive clinical outcomes in

significantly fewer sessions (Thatcher, 2013). With 4ZNF there have been reports of

successful clinical outcomes with less than 25 sessions (Collura, Guan, Tarrant, Bailey, &

Starr, 2010; Hammer, Colbert, Brown, & Ilioi, 2011; Wigton, 2008); whereas clinical

reviews and recent conference reports (J. L. Koberda, Moses, Koberda, & Koberda,

2012b; Rutter, 2011; Wigton, 2009, 2010a, 2010b, 2013; Wigton & Krigbaum, 2012)

suggest 19ZNF can result in positive clinical outcomes, as well as QEEG normalization,

in as few as 5 to15 sessions. Therefore a NF technique which shows promise to bring

clinical improvement in fewer sessions – thereby reducing treatment cost – deserves

empirical study.

Currently in the peer-reviewed published literature, there are a couple of

descriptive and clinical review articles about the 19ZNF model (Thatcher, 2013; Wigton,

4

2013) and two single case study reports (Hallman, 2012; J. L. Koberda et al., 2012a);

however rigorous scientific studies evaluating 19ZNF have not been found, which poses

a gap in the literature. Therefore, before the question of efficiency and number of

sessions is examined, first its efficacy should be established. NF and ZNF efficacy has

been discussed in the literature as having the desired effect in terms of improved clinical

outcomes (La Vaque et al., 2002; Thatcher, 2013; Wigton, 2013), a definition that fits

well within the scope of this research. In this study, there are two types of clinical

outcome measures; one type (clinical assessments) is a set of psychometric tests designed

to measure symptom severity and/or improvement, the other type (QEEG z-scores)

provides a representative measure of electrocortical dysfunction and/or improvement.

Thus, this dissertation is intended to address efficacy of 19ZNF in a clinical setting,

through a retrospective evaluation of clinical outcomes, as measured by clinical

assessments and QEEG z-scores.

Problem Statement

It is not known, by way of statistical evaluation of either clinical assessments or

QEEG z-scores, if 19ZNF is an effective NF technique. This is an important problem

because 19ZNF is a new NF model currently in use by a growing number of practitioners,

yet scientific research investigating its efficacy is lacking. According to an Efficacy Task

Force, established by the two primary professional organizations for NF and biofeedback

professionals, 1 anecdotal reports (regardless of how many) are insufficient as a basis for

1 The primary professional societies for neurofeedback and biofeedback are the International

Society for Neurofeedback and Research (ISNR; www.isnr.org) and the Association for Applied

Psychophysiology and Biofeedback (AAPB; www.aapb.org).

5

determining treatment efficacy, and uncontrolled case studies are scientifically weak (La

Vaque et al., 2002). Therefore, scientific evidence of efficacy for 19ZNF is needed.

The identified population for this study is made up of those seeking NF services

(both adults and children), and those who become NF clients. These individuals may

have an array of symptoms, which adversely affect their daily functioning; they may also

have previously diagnosed mental health disorders. When seeking NF services these

individuals must choose among a variety of NF models. However the dearth of scientific

literature regarding 19ZNF limits the information available to inform that decision-

making process. Therefore, it is vital that both NF clinicians and clients have empirically

derived information regarding the clinical value and efficacy of this new NF technique.

Consequently, the problem of this empirical gap impacts the NF clinician and client alike.

The goal of this research is to contribute in providing a first step towards addressing this

research gap.

Purpose of the Study

The purpose of this quantitative, retrospective, one-group, pretest-posttest study

research was to compare the difference between pre and post clinical assessments and

QEEG z-scores data, before and after 19ZNF sessions, from archived data of a private

neurofeedback practice in the Southwest region of the United States. The comparisons

were accomplished via statistical analysis appropriate to the data (i.e. paired t tests), and

will be further discussed in the Data Analysis section of Chapter 3. The independent

variable is defined as the 19ZNF, and the dependent variables are defined as the standard

scaled scores of three clinical assessments and QEEG z-score data. The clinical

assessments measure symptoms of attention, behavior, and executive function, whereas

6

the z-scores provide a representative measure of electrocortical function. The full scopes

of the assessments are further outlined in the Instrumentation section of Chapter 3.

Given the retrospective nature of this study, there were no individuals, as subjects,

with which to interact. However the target population group is considered to be adults

and children with clinical symptoms of compromised attention, behavior, or executive

function, who are interested in NF as an intervention for improvement of those

symptoms. This pretest-posttest comparison research contributes to the NF field by

conducting a scientific study, using quantitative group methods, to address the efficacy of

the new 19ZNF model.

Research Questions and Hypotheses

If the problem to be addressed is a lack of scientific evidence demonstrating

efficacy of 19ZNF, the solution lies in evaluating its potential for improving clinical

outcomes as measured by clinical assessments and electrocortical metrics. Therefore

research questions posed in terms of clinical symptomology and cortical function

measures is a reasonable approach. For this research the independent variable is the

19ZNF and the dependent variables are clinical outcomes, as measured by the scaled

scores from three clinical assessments and z-scores from QEEG data. The clinical

assessments are designed to measure symptom severity of attention, behavior, and

executive functioning, and the z-scores are a representational measure of electrocortical

function. The data gathering, scores calculation, and, data analysis were conducted by the

researcher.

7

The following research questions guided this study:

R1a. Does 19ZNF improve attention as measured by the Integrated Visual and

Auditory continuous performance test (IVA; BrainTrain, Incorporated,

Chesterfield, VA)?

Ha1a: The post scores will be higher than the pre scores for the IVA

assessment.

H01a: The post scores will be lower than, or not significantly different

from, the pre scores of the IVA assessment.

R1b. Does 19ZNF improve behavior as measured by the Devereux Scale of

Mental Disorders (DSMD; Pearson Education, Incorporated, San Antonio, TX)?

Ha1b: The post scores will be lower than the pre scores for the DSMD

assessment.

H01b: The post scores will be higher than, or not significantly different

from, the pre scores of the DSMD assessment.

R1c. Does 19ZNF improve executive function as measured by the Behavior

Rating Inventory of Executive Functioning (BRIEF; Western Psychological

Services, Incorporated, Torrance, CA)?

Ha1c: The post scores will be lower than the pre scores for the BRIEF

assessment.

H01c: The post scores will be higher than, or not significantly different

from, the pre scores of the BRIEF assessment.

R2. Does 19ZNF improve electrocortical function as measured by QEEG z-scores

(using the Neuroguide Deluxe software, Applied Neuroscience Incorporated, St.

8

Petersburg, FL), such that the post z-scores are closer to the mean than pre z-

scores?

Ha2: The post z-scores will be closer to the mean than the pre z-scores.

H02: The post z-scores will be farther from the mean, or not significantly

different from, the pre z-scores.

See as follows Table 1.1, outlining the research questions and variables.

Table 1.1

Research Questions and Variables

Research Questions Hypotheses Variables Instrument(s) 2. 1a. Does 19ZNF improve

attention as measured by

the IVA?

The post scores will be

higher than the pre scores

for the IVA assessment.

IV: 19ZNF

DV: IVA standard scale

scores

IVA

computerized

performance test

1b. Does 19ZNF

improve behavior as

measured by the DSMD?

The post scores will be

lower than the pre scores

for the DSMD

assessment.

IV: 19ZNF

DV: DSMD standard

scale scores

DSMD

rating scale

1. 1c. Does 19ZNF improve executive function as

measured by the BRIEF?

The post scores will be

lower than the pre scores

for the BRIEF

assessment.

IV: 19ZNF

DV: BRIEF standard

scale scores

BRIEF

rating scale

2. 2. Does 19ZNF improve electrocortical function

as measured by QEEG z-

scores such that the post

z-scores are closer to the

mean than pre z-scores?

The post QEEG z-scores

will be closer to the mean

than the pre z-scores.

IV: 19ZNF

DV: QEEG

z-scores

QEEG

z-score data generated

from Neuroguide

software

Advancing Scientific Knowledge

The theoretical framework of NF is the application of operant conditioning upon

the EEG, which leads to electrocortical changes, and in turn, better brain function and

clinical symptom improvement; moreover, studies evaluating traditional NF have

9

demonstrated its efficacy (Arns et al., 2009; Pigott et al., 2013). The 19ZNF model is

new, and experiencing increased use in the NF field, yet efficacy has not been established

via empirical investigation. There is a gap in the literature in that the only peer-reviewed

information available to date, regarding 19ZNF, are reviews, clinical report presentations,

and single case studies. Also noted as typically absent from traditional NF studies are

analyses of pre-post QEEG data (Arns et al., 2009); this lack of pre-post QEEG data

continues in the QNF literature as well. This, then, poses a secondary gap, in terms of

methodology, which this study has the potential to fill.

The clinical condition most researched for demonstrating traditional NF efficacy

is ADHD (Pigott et al., 2013), which includes cognitive functions of attention and

executive function. These issues also lead to some associated behavioral problems with

adverse impacts in instructional settings that are also treated with 19ZNF. Therefore,

addressing efficacy of 19ZNF with clinical assessments designed to measure these

constructs, will contribute to filling the gap of what is not known about this new NF

model, within a framework related to cognition and instruction. If efficacy is

demonstrated, the theory of operant conditioning, upon which NF is founded, may be

expanded to include 19ZNF.

Significance of the Study

The 19ZNF model is theoretically distinctly different from traditional NF in that it

targets real-time QEEG z-scores with a goal of normalizing QEEG metrics (as indicated

by clinical symptom presentation) rather than only increasing or decreasing targeted brain

frequencies. This model has been in existence for five years and its use by NF clinicians

is rapidly growing. Thus far, other than two qualitatively-oriented, single case study

10

reports (Hallman, 2012; J. L. Koberda et al., 2012a), there are no empirical group studies,

with a quantitative methodology, studying the efficacy of 19ZNF in peer-reviewed

literature. The significance of this study is that it aims to fill this significant gap manifest

as a dearth of 19ZNF efficacy studies.

Moreover, few NF studies include analysis of EEG measures as an outcome

measure (Arns et al., 2009). Therefore demonstrating how z-scores from QEEG data can

be used for group comparison studies, in a way not previously explored, will benefit the

scientific community. Thus, this research has the potential for opening doors for further

research.

It was expected the findings would demonstrate 19ZNF results in improved

clinical outcomes, as measured by clinical and QEEG assessments; thus demonstrating

efficacy. Potential NF clients will benefit from this contribution of what is known about

19ZNF by having more information upon which to base decisions for what type of NF

they wish to pursue. The potential effect of these results may provide the start of an

evidence-based foundation for its use. This foundation may lead to a greater acceptance

of what may be a more efficient (and thereby more economical) NF model, as well as

foster the needed additional scientific research of 19ZNF.

Rationale for Methodology

The field of clinical psychophysiology makes use of quantifiable variables and the

associated research should include specific independent variables, as well as dependent

variables that relate to treatment response (e.g. clinical assessments) and the measured

physiological component (e.g. EEG metrics) (La Vaque et al., 2002). Yet, many NF

studies do not use the EEG metric as a psychophysiologic measure, but rather provide

11

reports, which are more qualitative in nature. Therefore, there is a need for NF research,

with sound quantitative methodologies, using QEEG data as an outcome measure.

Currently, the available 19ZNF studies are in the form of qualitative research

(Hallman, 2012; J. L. Koberda et al., 2012a). This literature entails presenting data, from

single case studies, in the form of unstructured subjective reports of symptom

improvement and graphical images of before and after QEEG findings, where the

improvement is represented by a change in color on the picture (without statistical

analysis of data). However, for this dissertation, the goal is to explore statistical

relationships between the variables under investigation. The strength of quantitative

methodologies, including quasi-experimental research, is that they provide sufficient

information, regarding the relationship of the investigation variables, to enable the study

of the effects of the independent variable upon the dependent variable (Carr, 1994); this

is suitable in the evaluation of a quantitative technology such as 19ZNF.

As previously stated, for this research the independent variable is specified as

19ZNF. The dependent variables in this study are continuous variables in the form of

standard scores from clinical assessments (IVA, DSMD, and BRIEF) and z-scores from

QEEG data. The alternative hypotheses for all research questions predict a directional

significant difference between the means of the pre and the post values for all dependent

variables. Therefore, a quantitative methodology is appropriate for this dissertation.

Nature of the Research Design for the Study

This quasi-experimental research used a retrospective one-group, pretest-posttest

design. When the goal of research is to measure a modification of a behavior pattern, or

internal process that is stable and likely unchangeable on its own, the one-group pretest-

12

posttest design is appropriate (Kerlinger, 1986). In this type of design the dependent

variable pretest measures are compared to the posttest values for each subject, thus

comparing the members of the group to themselves rather than to a control or comparison

group (Kerlinger, 1986). Consequently, the group members become their own control,

hence reducing the potential for extraneous variation due to individual-to-individual

differences (Kerlinger & Lee, 2000). Moreover, the size of the treatment effect can be

estimated by analyzing the difference between the pretest to the posttest measures

(Reichardt, 2009). Therefore, this design as well as a quantitative methodology, is well

suited to evaluate the pre-post outcome measures from a clinical setting.

The rationale for this being a retrospective study is based on the fact that data

available for analysis came from pre-existing archived records, which frequently provides

a rich source of readily accessible data (Gearing, Mian, Barber, & Ickowicz, 2006).

Within the pool of available data, a sample group was gathered for which various pre and

post assessments were performed during the course of 19ZNF treatment. As depicted in

Figure 1.1, an initial group was formed for which pre-post QEEG assessments and z-

scores were available, and for which either the IVA, DSMD, or BRIEF pre-post

assessment data was also available (n = 21). From this collection three additional groups

were formed: One group for the IVA data (n = 10), a second group for the DSMD data (n

= 14), and a third group for the BRIEF data (n = 12). Therefore, using a one-group

pretest-posttest design with these identified groups is fitting. The independent variable is

the 19ZNF and the dependent variables are the data from the clinical assessments and

QEEG files (IVA, DMSD, BRIEF, and z-scores).

13

Formation of Sample Groups

Figure 1.1. Illustration of how the sample groups were formed. The

total number of subjects in the sample is 21. However, out of those

21, some may have multiple assessments, therefore subjects may be

in more than one clinical assessment group.

Definition of Terms

The following terms were used operationally in this study.

19ZNF. 19-channel z-score NF is a style of NF using all 19 sites of the

International 10-20 system, where real-time QEEG metrics are incorporated into the NF

session in the form of z-scores (Collura, 2014). The goal is for the targeted excessive z-

score metrics (whether high or low) to normalize (move towards the mean). The 19ZNF

cases included in this study are those for which the assessed clinical symptoms

corresponded with the z-score deviations of the QEEG findings, such that a treatment

goal of overall QEEG normalization was clinically appropriate. While the 19ZNF

protocols are individually tailored to the clinical and QEEG findings, the same treatment

goal always applies, that is the overall QEEG normalization. Therefore, the underlying

19ZNF protocol of overall QEEG normalization is consistent for all cases.

14

Absolute power. A QEEG metric which is a measure of total energy, at each

electrode site, for a defined frequency band (Machado et al., 2007); may be expressed in

terms of microvolts, microvolts squared, or z-scores when compared to a normative

database (Collura, 2014).

Amplifier. The equipment that detects, amplifies, and digitizes the brainwave

signal (Collura, 2014). The term is more correctly referred to as a differential amplifier

because the electrical equipment measures the difference between two signal inputs

(brainwaves from electrode locations) (Collura, Kaiser, Lubar, & Evans, 2011).

Amplitude. A measure of the magnitude or size of the EEG signal; and is

typically expressed in terms of microvolts (uV) (Collura et al., 2011). This can be thought

of as how much energy is in the EEG frequency.

Biofeedback. A process of learning how to change physiological activity with the

goal of improving health and/or performance (AAPB, 2011). A simple example of

biofeedback is the act of stepping on a scale to measure one’s weight.

Behavior Rating Inventory of Executive Functioning (BRIEF). The BRIEF,

published by Western Psychological Services, Incorporated (Torrance, CA), is a rating

scale. It has forms for both children and adults, and is designed to assess behavioral,

emotional, and metacognitive skills, which broadly encompass executive skills, rather

than measure behavior problems or psychopathology (Donders, 2002). The test results

are expressed as T scores for various scales and sub-scales (with clinically significant

scores ≥ 65), and lower scores indicate improvement upon re-assessment. The composite

and global scales of Behavior Regulation Index, Metacognition Index, and Global

Executive Composite were included in this study.

15

Coherence. A measure of similarity between two EEG signals, which also

reflects the degree of shared information between the sites; computed in terms of a

correlation coefficient, which varies between .00 to 1.00 (Collura et al., 2011).

Devereux Scale of Mental Disorders (DSMD). The DSMD, published by

Pearson Education, Incorporated (San Antonio, TX), is a rating scale. It is designed to

assess behavior problems and psychopathology in children and adolescents (Cooper,

2001). The test results are reported in the form of T scores for various scales and sub-

scales (with clinically significant scores ≥ 60), and lower scores indicate improvement

upon re-assessment. The composite and global scales of Externalizing, Internalizing, and

Total were included in this study.

Electrode. Central to NF is the detection and analysis of the EEG signal from the

scalp. In order to record brainwaves it is necessary to attached metallic sensors

(electrodes) to the scalp and/or ears (with a paste or gel) to facilitate this process (Collura,

2014).

Electroencephalography (EEG). A recording of brain electrical activity (i.e.

brainwaves) using differential amplifiers, measured from the scalp (Collura et al., 2011).

The information from each site or channel is digitized to be viewed as an oscillating line,

such that all channels can be viewed on a computer screen at one time.

Fast Fourier transform (FFT). The conversion of a series of digital EEG

readings into frequency ranges/bands, which can be viewed in a spectral display. Just as

different frequencies of light can be seen when filtered through a prism, so too can EEG

elements be isolated when filtered through a FFT process into different frequency bands

(Collura, 2014).

16

Frequency / frequency bands. The representation of how fast the signal is

moving, expressed in terms of Hertz (Hz) (Collura, 2014) and commonly arranged in

bandwidths, also referred to as bands. Generally accepted frequency bands are delta (1-4

Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (12-25 Hz), and high beta (25-30 Hz); the beta

band may be broken down into smaller bands of beta1 (12-15 Hz), beta2 (15-18 Hz), and

beta3 (18-25 Hz), and the alpha band may be divided into alpha1 (8-10 Hz) and alpha2

(10-12 Hz).

Gaussian. Referring to the normal distribution and/or normal curve (Thatcher,

2012).

Hedges’ d. An effect size, belonging to the d family indices (along with Hedges’

g), which use the standard score form of the difference between the means; therefore it is

similar to the Cohen’s d, with the same interpretation (Hunter & Schmidt, 2004).

However, when used with small sample sizes, both the Cohen’s d and Hedges’ g, can

have an upward bias and be somewhat over-inflated; however the Hedges’ d includes a

correction for this bias (Hunter & Schmidt, 2004). Therefore, in studies with smaller

sample sizes, the use of the Hedges’ d provides a more conservative, and likely more

accurate effect size. Also, complicating this issue is confusion in the literature regarding

the use of the designator g or d for which particular Hedges index, and/or which

calculation does or does not include the correction factor (Hunter & Schmidt, 2004). For

example, frequently Hedges’ g is described as adjusting for small samples sizes;

however, this is only true if the calculation used includes the correction factor. Moreover,

there are even variations in the literature of the correction equation which is applied. As a

result, the only way to know which calculation is actually being used is for the Hedges’

17

index equation to be explicitly reported. To that end, for this study, the Hedges’ d

definition/calculation will be that used in the Metawin 2.1 meta-analysis software

(Rosenberg, Adams, & Gurevitch, 2000). In this context the Hedges’ d is calculated by

multiplying the Hedges’ g by the correction, which is sometimes referred to as J.

Where and therefore .

Hertz (Hz). The number of times an EEG wave oscillates (moves up and down)

within a second; commonly expressed as cycles per second (Collura, 2014).

International 10-20 System. A standardized and internationally accepted method

of EEG electrode placement locations (also referred to as sites) on the scalp. The

nomenclature of 10-20 derives from electrode locations being spaced a distance of either

10% or 20% of the measured distance from certain landmarks on the head. The system

consists of a total of 19 sites, with eight locations on the left, eight on the right, and three

central sites found on the midline between the right and left side of the head (Collura,

2014).

Visual and Auditory + Plus Continuous Performance Test (IVA). The IVA,

developed and published by BrainTrain Incorporated (Chesterfield, VA), is a

computerized interactive assessment. It is normed for individuals over the age of 5, and it

is designed to assess both auditory and visual attention and impulse control with the aim

to aid in the quantification of symptoms and diagnosis of ADHD (Sanford & Turner,

2009). The test results are reported in the form of quotient scores for various scales and

sub-scales (with clinically significant scores ≤ 85), and higher scores indicate

improvement upon re-assessment. The global and composite scales of Full Scale

18

Attention Quotient, Auditory Attention Quotient, and the Visual Attention Quotient were

included in this study.

Joint time frequency analysis (JTFA). A method of digitizing the EEG signal

which allows for moment-to-moment (i.e. real time) measures of EEG signal changes

(Collura, 2014).

Montage. The configuration of the electrodes and software defining the reference

point and electrode linkages, for the differential recording of the EEG signals (Thatcher,

2012). For example, in a linked-ears montage, the signal for each electrode site is

referenced to the signal of the ear electrodes linked together. In a Laplacian montage, the

signal for each electrode site is referenced to the signal of the weighted average of the

surrounding electrode sites.

Neurofeedback. An oversimplified, yet accurate, definition of neurofeedback is

that it is simply biofeedback with brainwaves. Generally, it is an implicit learning process

(involving both operant and classical conditioning) where changes in brainwave

signal/patterns, in a targeted direction, generates a reward (a pleasant tone and change in

a video animation) such that the desired brainwave events occur more often (Collura,

2014; Thatcher, 2012).

Normalization. In the context of NF, refers to the progression of excessive z-

scores towards the mean (i.e. z = 0), meaning the NF trainee’s EEG is moving closer to

the EEG range of normal (i.e. typical) individuals of his/her age (Collura, 2014). Thus,

the concept of normalization is generally accepted to be when the z-scores of the QEEG

move towards the mean (i.e. in the direction of z = 0).

19

Power spectrum. The distribution of EEG energy across the frequency bands,

typically from 1 Hz to 30 Hz and frequently displayed as a line graph, histogram, or color

topographic (i.e. visual representations of the numerical data) images (Collura, 2014).

Phase. The temporal relationship between two EEG signals, reflecting the speed

of shared information (Collura et al., 2011).

Protocol. The settings designated in NF software, informed by a treatment plan,

which determines how the NF proceeds. This establishes parameters such as metrics (e.g.

absolute vs. relative power), direction of training (i.e. targeting more or less), length of

session, and other decision points in the NF process (Collura, et al., 2011).

Quantitative EEG (QEEG). The numerical analysis of the EEG such that it is

transformed into a range of frequencies as well as various metrics such as absolute

power, relative power, power ratios, asymmetry, coherence, and phase (Collura, 2014;

Thatcher, 2012). The data is typically made up of raw numbers, statistical transforms into

z-scores, and/or topographic images (Collura, 2014). As a dependent variable in this

study, QEEG z-scores are considered a representational measure of electrocortical

function. The metrics of absolute power, relative power, and coherence were included.

Relative power. A QEEG metric representing the amount of energy, divided by

the total energy, at each electrode site, for a defined frequency band. It reflects how much

energy is present compared to all other frequencies (Collura, 2014).

Assumptions, Limitations, Delimitations

This section identifies the assumptions and specifies the limitations, together with

the delimitations of the study. The following assumptions were present in this study:

20

1. It was assumed that traditional neurofeedback is deemed efficacious as

discussed and demonstrated in the literature (Arns et al., 2009; Pigott et

al., 2013).

2. It was assumed that the subjects are representative of the population of those

who seek NF treatment for various mental health disorders; thus allowing

for results to be generalized to that population (Gravetter & Wallnau,

2010).

3. It was assumed the sample is homogeneous and selected from a population

that fits the normal distribution such that the sample means distribution are

also likely to fit a normal distribution (Gravetter & Wallnau, 2010).

4. It was assumed that responses provided on rating scale instruments accurately

reflect perceived or remembered observations, thus minimizing bias for

over or under-reporting of observations (Kerlinger & Lee, 2000).

The following limitations were present in this study:

1. Research design elements. A general limitation of designs that incorporate a

pretest-posttest formulation is primarily related to the passage of time

between administering the pre and post assessments (Kerlinger & Lee,

2000). Factors such as history and maturation cannot be controlled for;

therefore it is not possible to know whether or not they have impacted the

dependent variable measures (Hunter & Schmidt, 2004). However, for this

study the time between the pre and post assessment is relatively short, and

can be measured in terms of weeks. Therefore, the impact of time-related

confounds were anticipated to be minimal. Further limitations which also

21

must be recognized are a lack of comparison to a traditional NF group, and

a lack of a randomized control group.

2. Small sample size. Larger sample sizes are preferred in order to allow for

stronger statistical analysis and more generalizability (Gravetter &

Wallnau, 2010). Given this study used pre-existing archived data, the

number of samples were restricted to what was found in the files; thus

there was no option to increase sample size. Though, as detailed in

Chapter 3, the sample sizes for each group provided sufficient power to

allow for adequate statistical analysis.

The following delimitations will be present in this study:

1. This study was delimited to the scope of the surface formulation of 19ZNF.

Therefore, it did not include in its scope other variations of 19-channel NF

models, founded in inverse solution theories, such as low-resolution brain

electromagnetic tomography (LORETA) ZNF or functional magnetic

resonance imaging (fMRI) tomography NF models.

2. This study was delimited to a scope of NF research data collected primarily

from clinical settings, as opposed to laboratory-based experimental

research.

3. The academic quality standards for this dissertation delimit the literature

reviewed for this study to exclude certain non-peer-reviewed sources (i.e.

NF industry newsletters).

In spite of the above stated assumptions, limitations, and delimitations, this study

has potential to be of value to the scientific and neurofeedback community. Given the

22

data for this research comes from a real-world clinical setting, the findings of this study

still contribute to advancing the scientific knowledge of 19ZNF.

Summary and Organization of the Remainder of the Study

In summary, while NF has a history spanning over 40 years, it is only now

gaining acceptance as an evidence-based mental health intervention (Pigott et al., 2013).

Various models of NF have been developed over the years, with one of the newest

iterations including 19ZNF, which is reported to lead to improved clinical outcomes in

fewer sessions than other models (Thatcher, 2013; Wigton, 2013). However, there are

significant gaps in terms of peer-reviewed literature and research, such that efficacy of

19ZNF has yet to be established. This dissertation intends to fill these gaps by addressing

efficacy of 19ZNF, in a clinical setting, using a comparison of pretest-posttest measures

of clinical assessments and QEEG z-scores.

The following chapters include the literature review in Chapter 2 and a

description of the methodology, research design, and the procedures for the study in

Chapter 3. The literature review first explores the background and history of the problem,

then discusses theoretical foundations and conceptual frameworks, and finally reviews

the literature pertaining to the NF models relevant to this study. Of note is the necessity

of a significantly expanded theoretical/conceptual section. The methodological

foundations of a treatment intervention based in EEG/QEEG technology, combined with

the need to explore the theoretical foundations of three different NF models (traditional,

QNF, and ZNF), require more in depth coverage of the topics involved in that section.

23

Chapter 2: Literature Review

Introduction and Background to the Problem

The focus of this study was to explore the efficacy of 19ZNF in a clinical setting,

through the use of clinical assessments and QEEG z-scores as outcome measures. Yet, a

review of the literature is necessary to place this research into context of NF theory and

the various models that have come before 19ZNF. This literature review consists of three

sections.

The first section addresses the history and background of NF in general and

specifically introduces ZNF, as well as comments on how the gap in research for 19ZNF

evolved into its current form. The second section focuses on the theoretical foundations

and conceptual frameworks of NF and QEEG. First, an overview of the foundations of

EEG and QEEG is presented. Next, an overview of learning theory as applied to NF is

discussed. Then, the theoretical frameworks supporting the different models of NF

(traditional, QNF, and ZNF) are reviewed. Last, key themes of NF concepts relevant to

this dissertation including applications of QNF, the development of 4ZNF, and finally the

emergence of 19ZNF are examined. Also included in this section is a review of suitable

outcome measures for use in ZNF research, with special attention paid to prior NF

research regarding performance tests, rating scale assessments, and QEEG z-scores, as

outcome measures.

Of note for this literature review is the necessity to include reviews of conference

oral and poster presentations (which are subject to a peer-review acceptance process).

While inclusion of these sources may be an unusual dissertation strategy, it is necessary

due to the scarcity of sources in the peer-reviewed published literature regarding ZNF

24

models. To exclude these sources would be to limit the coverage of the available

literature regarding the NF model which is the focus of this dissertation (19ZNF).

The literature for this review was surveyed through a variety of means. The

researcher’s personal library (from nearly fifteen years of practicing in the NF field)

served as the foundation for the literature search. Then, this was expanded through online

searches of various university libraries via academic databases such as Academic Search

Complete, PsycINFO, PsycARTICLES, and MEDLINE, with search strings of

combinations of terms such as NF, QEEG, EEG biofeedback, z-score(s). Additionally,

the databases of various industry specific journals, such as the Journal of Neurotherapy,

Clinical EEG and Neuroscience, as well as the Applied Psychophysiology and

Biofeedback journal were queried with similar search terms. Moreover, with the specified

journals, names of leading authors in the QNF and ZNF field (e.g. Koberda, Surmeli,

Walker) were used for search terms.

Historical overview of EEG and QEEG. A review of NF literature reveals a

common theme that the deepest roots of NF go back only as far as Hans Berger’s (1929)

discovery of EEG applications in humans. However, the antecedents of EEG technology

can actually be traced back as far as the 1790s with the work of Luigi Galvani and the

discovery of excitatory and inhibitory electrical forces in frog legs, leading to the

recognition of living tissue having significant electrical properties (Bresadola, 2008;

Collura, 1993). The next notable application occurred when Richard Caton (1875) was

the first to discover electrical activity in the brains of monkeys, rabbits, and cats, and to

make observations regarding the relationship of this activity to physiological functions

(Collura, 1993). Yet for applications of EEG in humans, Berger is generally recognized

25

as the first to record and report on the phenomenon. Thus, it would be most correct to

consider Caton as the first electroencephalographer, and Berger as the first human

electroencephalographer (Collura, 1993). Moreover, Berger’s contributions were

significant as they spurred a plethora of research and technological advancements in EEG

technology in the 1930s and 1940s worldwide. Of note is that Berger not only identified

both alpha and beta waves, but he was also the first to recognize the EEG signal as being

a mixture of various frequencies which could be quantitatively estimated, and spectrally

analyzed through the use of a Fourier transform, thus paving the way for QEEG

technology as well (Collura, 2014; Thatcher, 2013; Thatcher & Lubar, 2009).

Even while there was an understanding of multiple components to the EEG signal

as early as the 1930s, the advent of computer technology was necessary to make possible

QEEG advances (Collura, 1995); for example, the incorporation of normative databases

in conjunction with QEEG analysis. Therefore, the historical landmarks of EEG

developments can trace the modern start of normative database applications of QEEG

back to the 1970s with the work of Matousek and Petersen (1973) as well as John (1977)

(Pizzagalli, 2007; Thatcher & Lubar, 2009). However, while work exploring NF

applications with QEEG technology began in the 1970s, its wider acceptance and use in

the NF field was not until closer to the mid-1990s (Hughes & John, 1999; Thatcher &

Lubar, 2009). Here too, advances in computer technology, whereby personal computers

were able to process more data in less time, made way for advances in the clinical

applications of NF.

Historical overview of NF. The historical development of neurofeedback dates

back to the 1960s and early 1970s when researchers were studying the EEG activity in

26

both animals and humans. In these early days, Kamiya (1968, 1969) was studying how

humans could modify alpha waves, and Sterman and colleagues (Sterman et al., 2010;

Wyricka & Sterman, 1968) were able to demonstrate that cats could generate sensory

motor rhythm, which led to the discovery that this process could make the brain more

resistant to seizure activity; this eventually carried over to work in humans (Budzynski,

1999). Later, Lubar (Lubar & Shouse, 1976), expanded on Sterman’s work, and began

studies applying NF technology to the condition of attention disorders. This work led to

an expansion of clinical applications of neurofeedback to mental health issues such as

ADHD, depression and anxiety, using a training protocol generally designed to increase

one frequency (low beta or beta, depending on the hemisphere) and decrease two other

frequencies (theta and high beta) (S. Othmer, Othmer, & Kaiser, 1999).

Then, in the 1990s QEEG technology began gaining wider acceptance in the NF

community, for the purpose of guiding the development of protocols for NF (Johnstone,

& Gunkelman, 2003). The use of normative referenced databases has been an accepted

practice in the medical and scientific community and the advantage it brings to

neurofeedback is the allowance for the comparison of an individual to a norm-referenced

population, in terms of z-scores, to identify measures of aberrant EEG activity (Thatcher

& Lubar, 2009). This made possible the development of models, which focused more on

the individualized and unique needs of the client rather than a one-size-fits-all model.

Consequently, during the ensuing decade, the QNF model began taking hold in the NF

industry. However, the primary number of channels incorporated in the amplifiers of the

time was still limited to only two.

http://www.citeulike.org/user/michaelbrewer/author/Johnstone:J
http://www.citeulike.org/user/michaelbrewer/author/Gunkelman:J

27

In 2006, the 4-channel – 4ZNF – technique was introduced. ZNF incorporates the

application of an age matched normative database to instantaneously compute z-scores,

via Joint Time Frequency Analysis (as opposed to the fast Fourier transform), making

possible a dynamic mix of both real-time assessment and operant conditioning

simultaneously (Collura et al., 2009; Thatcher, 2012). While the QNF of the 1990s held

as a common goal movement of the z-scores in the QEEG towards the mean, the advent

of ZNF brought with it the more frequent use of the term normalizing the QEEG or

normalization to refer to this process. It is now generally acknowledged that the term

normalization, when used to describe the process of ZNF, refers to the progression of the

z-scores towards the mean (i.e. z = 0), meaning that the NF trainee’s EEG is moving

closer to the EEG range of normal (i.e. typical) individuals of his/her age. But by 2009

the 4ZNF model was further enhanced to include the availability of up to all 19 electrode

sites in the International 10-20 system.

This surface potential 19ZNF greatly expands the number of scalp locations and

measures, including the ability to train real-time z-scores using various montages such as

linked-ears, averaged reference, and Laplacian, as well as simultaneous inclusion of all

connectivity measures such as coherence and phase lag. This, then, makes possible the

inclusion of all values from the database metrics for any given montage (as many as a

total of 5700 variables) in any protocol (Collura, et al., 2009). But the advent of 19ZNF

not only increases the number and types of metrics available to target, it also brought two

major changes to the landscape of NF. First, it established a new model wherein the

target of interest for the NF is the QEEG calculated z-scores of the various metrics

(frequency/power, coherence, etc.), rather than the amplitude of particular frequency

28

bands (theta, beta, etc.). Second, it changed the makeup of a typical NF session. In either

the conventional QNF model, or 4ZNF, the clinician will develop a protocol guided by

the QEEG findings, but will generally employ the same protocol settings repeatedly for

multiple NF sessions until the next assessment QEEG is scheduled. However with

19ZNF, in every session the clinician can acquire and process QEEG data, compare the

pre-session data to past session data, then design an individualized z-score normalization

protocol based on that day’s QEEG profile, and then perform a 19ZNF session, all within

an hour (Wigton, 2013). Thus, each 19ZNF session uses a protocol unique to the client’s

brainwave activity of that day, providing further tailoring of the NF to the individual

needs of the client, on a session-by-session basis. This, then, brought a new dynamic to

the normalization model of NF such that z-scores (rather than amplitude of frequencies)

could be targeted, on a global basis, so as to make possible a goal of normalizing all the

QEEG z-scores (when clinically appropriate) in the direction of z = 0.

How problem/gap of 19ZNF research evolved into current form. Over its

more than 40-year history NF has frequently been criticized as lacking credible research,

as evident by Loo and Barkley’s (2005) critique. Nevertheless, even Loo and Makeig

(2012) concede recently the research has improved. For example, Arns et al. (2009)

conducted the first comprehensive meta-analysis of NF, covering 1194 subjects,

concluding that it was both efficacious and specific as a treatment for ADHD, with large

to medium effect sizes for inattention and impulsivity, respectively. Then, in a research

review sponsored by the International Society for Neurofeedback and Research (ISNR),

in what is a comprehensive review of controlled studies of NF, Pigott et al. (2013)

evaluated 22 studies to conclude that NF meets the criteria of an evidence-based

29

treatment for ADHD. This review further documents that NF has been found to be

superior to various experimental group controls, shows equivalent effectiveness to

stimulant medication, and leads to sustained gains even after termination of treatment.

However, as encouraging as this body of research is, it is limited in that the model

covered by these studies is largely limited to one of the most traditional models of NF

(theta/beta ratio NF) and only addresses a single condition of ADHD. Missing from these

comprehensive reviews and meta-analyses are newer QNF models, which have been in

use since the 1990s, and are frequently employed for a wider range of disorders in

addition to ADHD. Yet, that is not to say that QNF is devoid of research. In fact, from

2002 to 2013 there are at least 20 studies in peer-reviewed literature covering the QNF

model, yet there is great diversity in the different conditions treated in these studies, as

well as a greater use of individualized, custom-designed protocols; hence making meta-

analysis of this collection of research less feasible. Nonetheless, these studies do

represent a body of research pointing to the efficacy of the QNF model.

Yet, when it comes to the newest models of surface ZNF, there is no such

collection of research in the literature. There exist only two studies (Collura et al., 2010;

Hammer et al., 2011) which evaluate sample groups of the 4ZNF model, and the Collura

et al. report is mostly descriptive in nature. This, then, leaves only one experimental

study. There is one dissertation on 4ZNF (Lucido, 2012), but it too is a single case study.

Regarding 19ZNF, as of this writing, there are only two peer-reviewed published

empirical reports specifically evaluating surface potential 19ZNF (Hallman, 2012; J. L.

Koberda et al., 2012b) and those are only case study in nature.

30

Yet, this is not to say the peer-reviewed literature landscape is entirely devoid of

any mention of surface ZNF models. Nevertheless, what does exist is mostly information

about the technique in the form of review articles (Collura, 2008; Stoller, 2011; Thatcher,

2013; Wigton, 2013), chapters in edited books (Collura et al. 2009; Wigton, 2009), as

well as numerous qualitative oral and poster conference presentations since 2008. Of note

is a recent poster presentation (Wigton & Krigbaum, 2012), with a later expansion of that

work (Krigbaum & Wigton, 2013), which was a multicase empirical investigation of

19ZNF; however it primarily focused on a proposed research methodology for assessing

the degree of z-scores progression towards the mean. There also exist anecdotal

observations in the form of case reports in non-peer-reviewed publications and internet

website postings. Yet, while anecdotal observations and information from review and

case study reports are helpful for initial appraisals of a new model, quantitative statistical

analysis is needed to validate theories born of early qualitative evaluations, to counter a

lack of acceptance from the wider neuroscience community.

Much of the focus of discussions of 19ZNF is on the potential for good clinical

outcomes in fewer sessions than traditional NF (J. L. Koberda et al., 2012a; Rutter, 2011;

Thatcher, 2013; Wigton, 2009; Wigton, 2013). Though, before the question of number of

sessions is examined, first there should be an establishment of the efficacy of this

emerging model; because empirical studies evaluating the efficacy of 19ZNF are absent

from the literature. This dissertation was intended to fill this gap of knowledge, by

analyzing the efficacy of 19ZNF in a clinical setting.

31

Theoretical Foundations

Foundations of EEG and QEEG. Hughes and John (1999) discussed a decade-

long history, inclusive of over 500 EEG and QEEG related reports, the findings of which

indicate that cortical homeostatic systems underlie the regulation of the EEG power

spectrum, that there is a stable characteristic in healthy humans (both for age and cross-

culturally), and that the EEG/QEEG measures are sensitive to psychiatric disorders.

These factors made possible the application of Gaussian-derived normative data to the

QEEG metrics such that these measures are independent of ethnic or cultural factors,

which allow objective brain function assessment in humans of any background, origin, or

age. As a result, Hughes and John assert when using artifact-free QEEG data, the

probability of false positive findings are below that which would be expected by chance

at a p value of .0025. Thus, changes in the QEEG values would not be expected to occur

by chance, nor is there a likelihood of a regression to the mean of QEEG derived z-scores

because EEG measures, and the corresponding QEEG values, are not random. Since the

work of Hughes and John, well over a decade ago, there have been numerous studies

published in the literature further demonstrating the reliability and validity of QEEGs

(Cannon et al., 2012; Corsi-Cabrera, Galindo-Vilchis, del-Río-Portilla, Arce, & Ramos-

Loyo, 2007; Hammond, 2010; Thatcher, 2012; Thatcher & Lubar, 2009).

Learning theory as applied to NF. As has been stated, NF is also frequently

referred to as EEG biofeedback, and biofeedback has been defined simply as the process

whereby an individual learns how to change physiological activity (AAPB, 2011). As

Demos (2005) asserted, biofeedback is a two-way model such that 1) the physiologic

activity of interest is recorded, and 2) reinforcement is provided each time the activity

32

occurs; as a result, voluntary control of the targeted physiologic activity becomes

possible. On the surface this is a basic descriptor of operant conditioning. As a result, a

common practice in the literature is for NF to be referred to only as an operant

conditioning technique. However, the theoretical frameworks of NF are more correctly

framed as encompassing both classical and operant conditioning mechanisms (Collura,

2014; Sherlin, Arns, Lubar, Heinrich, Kerson, Strehl, & Sterman., 2011; Thatcher, 2012;

M. Thompson & Thompson, 2003). Operant conditioning – as first conceptualized by

Edward Thorndike (1911) with the Law of Effect, which holds that satisfying rewards

strengthens behavior, and as further advanced by B. F. Skinner (1953) – has as its

primary principle when an event is reinforced/rewarded it is likely to reoccur

(Hergenhahn, 2009); and for Skinner the reinforcer is anything that has contingency to a

response. It is important to note that operant conditioning relates to the learning of

volitionally controlled responses, motivation is necessary, and rewards need to be desired

or meaningful (M. Thompson & Thompson, 2003).

In contrast, classical conditioning, established by Ivan Pavlov (1928), differs in

that it deals with learning of reflexive or autonomic nervous responses. The primary

mechanism is based in the associative principles of contiguity and frequency such that the

presence of a dog’s food, which naturally elicits a salivation reflex, when paired

(contiguity) with a bell, repeatedly (frequency), will lead to the dog salivating upon the

presentation of only the bell (Hergenhahn, 2009). Thus, the pairing of two previously

unpaired events results in automatic learning in the form of classical conditioning. Yet, it

is important to note that while operant conditioning involves volitionally oriented

behavior modification, NF is a learning process which occurs largely outside of

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conscious awareness; in essence, an implicit learning process (Collura, 2014). As applied

to NF, the change in the EEG, as reflected in brainwave frequencies, patterns, or z-scores,

is the behavior which is modified as a result of the combined classical and operant

conditioning occurring in the NF session (Collura, 2014).

In this context then, successful NF involves a motivated trainee experiencing the

repeated pairing of meaningful auditory and/or visual reward signals, when the recorded

brainwaves fall in a targeted range. The reward signal is typically in the form of an

auditory tone (beep, chime, music) in combination with an animated visual display

(simple game-like displays or movies), which when aesthetically pleasant to the trainee

enhances and promotes the process. Some have noted the importance of additional

learning theory components such as shaping (Collura, 2014; Sherlin et al., 2011; M.

Thompson & Thompson, 2003), anticipation of future rewards (Thatcher, 2012), and

secondary reinforcers (Sherlin et al., 2011; M. Thompson & Thompson, 2003) to further

inform NF to varying degrees. These variations as applied to NF have served to generate

a range of NF models over the years; however the basic foundations of classical/operant

conditioning remain constant in all the models.

Traditional/amplitude-based models of NF. In NF, when the EEG is divided

into different frequency bands (alpha, beta, etc) the amplitude measures how much of that

frequency is present within the total EEG spectrum recording. The basic goal of

amplitude NF treatment models is to either increase or decrease the amplitude of a

particular frequency. These models are the longest-standing conceptualization of NF

techniques and for that reason, for the purposes herein, the term traditional will be used

to refer to these models of NF. The earliest traditional model of NF started with Kamiya’s

34

(1968) discovery in the early 1960s that human alpha waves could be increased and

trained to occur for increased periods of time. Next, Sterman and Fiar (1972) followed up

on this work by expanding the training Sterman had been conducting with cats to include

humans, with the first known case of resolving a seizure disorder in a person using NF. In

this model the goal was to increase the beta frequency of 12-15 Hz, also referred to as

sensorimotor rhythm (SMR), along the sensorimotor cortex of the brain. Others then

expanded on this model. For example, Lubar believed the model Sterman developed

would be applicable to children with attention disorders (Robbins, 2000). After a year-

long academic fellowship with Sterman, he moved on to develop his own model which

incorporated decreasing the theta frequency in addition to increasing beta (Robbins,

2000). Lubar and Shouse (1976) reported on the first use of this approach, which was the

foundation for what would become one of the most commonly reported and researched

protocols (for use with attention disorders) in the literature since the early 1990s; that of

the theta/beta ratio model.

Another example of a traditional NF model with roots to Sterman’s efforts is the

Othmer model (S. Othmer, Othmer, & Kaiser, 1999), employing a combination of

increasing beta (either 12-15 Hz or 15-18Hz) together with decreasing theta (4-7 Hz), and

a higher beta band (22-30 Hz); again with electrode placements primarily along the

sensorimotor cortex locations of the scalp. In the years since its introduction, there have

been different modifications and variations of the Othmer approach (S. F. Othmer &

Othmer, 2007). Nevertheless, consistent with traditional NF, this model makes use of

targeting the amplitudes of frequency bands in particular directions (i.e. make more or

less of targeted frequencies).

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While some built models based in the original findings of Sterman, others

expanded on Kamiya’s work, by developing models which targeted the increase of alpha

and/or theta frequencies (in parietal brain regions) to enhance relaxation and creative

states (Budzynski, 1999). Peniston and Kullkosky (1990, 1991) developed applications of

these approaches, which led to treatment models for alcoholism and posttraumatic stress

disorders. Yet still others, such as Baehr, Rosenfeld, and Baehr (1997), established

protocols targeted to balance alpha in the frontal regions as a treatment for depression.

While each of the above models targeted different frequencies with a variety of

protocols, consistent was a focus on changing the amount of the brainwave of interest;

the desired outcome is either greater or lesser amplitude of a target frequency. Moreover,

pre-treatment assessment of EEG activity to inform NF protocols is limited to nonexistent

in the majority of these models, with a typical one-size-fits-all approach. While selecting

the particular NF model for a treatment approach (i.e. theta-beta ratio versus alpha-theta

training) is informed by the presenting symptoms of each case, personalizing a NF

protocol to address the individual brainwave patterns of the client is not the focus of these

approaches.

QNF model of NF. A key focus of QNF is precisely tailoring the NF protocol,

based on the individual EEG baseline and symptom status of the client, as determined by

the QEEG, in conjunction with clinical history and presenting symptoms (Arns et al.,

2012). The primary premise of this approach is that localized cortical dysfunctions, or

dysfunctional connectivity between localized cortical areas, correspond with a variety of

mental disorders and presenting symptoms (Coben & Myers, 2010; Collura, 2010;

Walker, 2010a). When the EEG record of an individual is then compared to a normative

36

database representing a sample of healthy individuals, the resulting outlier data

(deviations of z-scores from the mean) help link clinical symptoms to brain dysregulation

(Thatcher, 2013). For example, when an excess of higher beta frequencies are found, the

typical associated symptoms include irritability, anxiety, and a lowered frustration/stress

tolerance (Walker, 2010a).

The conceptual framework of the stability of QEEG, as noted above, applies to

QNF in that a stable EEG is not expected to change without any intervention, thus the

changes seen as a result of QNF is not occurring by chance, but due to the operant

conditioning of the brainwaves as a result of the NF process (Thatcher, 2012). Therefore,

in the example of excess beta frequencies, when the symptoms of anxiety and irritability

are resolved after QNF, and the post QEEG shows the beta frequencies to be reduced

(closer to the mean), it is assumed the improvement in symptoms is due to the change in

the QEEG; thus representing improved electrocortical functioning (Arns et al., 2012;

Walker, 2010a). The term for this process, which has arisen secondary to QNF, is

generally referred to as normalization of the QEEG, or simply normalization (Collura,

2008; Surmeli & Ertem, 2009; Walker, 2010a). Consequently, the concept of

normalization is generally accepted to be when the z-scores of the QEEG move towards

the mean (i.e. z = 0).

It is also important to note that the QNF model, with its reliance on the QEEG to

guide the NF protocol, embraces the heterogeneity of QEEG patterns as discussed by

Hammond (2010). In understanding that a particular clinical symptom presentation may

be related to varied deviations in the QEEG, it quickly becomes apparent that each NF

protocol needs to be personalized to the client; as well as monitored and modified for

37

maximum treatment effect (Surmeli et al., 2012). This, then, results in different

electrophysiological presentations being treated differently, even if the overarching

diagnosis is the same. This clinical approach is supported through multiple reports in the

literature discussing how training the deviant z-scores towards the mean (i.e. normalize

the QEEG) in QNF results in the greatest clinical benefit (Arns et al., 2012; Breteler et

al., 2010; Collura, 2008; Orgim & Kestad, 2013; Surmeli et al., 2013; Surmeli & Ertem,

2009, 2010; Walker, 2009. 2010a, 2011, 2012a).

However, while the personalization of NF protocols aids in greater specificity in

client treatment, it creates methodological challenges for researching QEEG based NF

models; which will be discussed further below. When boiling down the elements of study

to a lowest common denominator, overall normalization of the QEEG is the only

common point of measurement. Therefore a reasonable tool, as a measure of change in

the QEEG, would be a value reflecting the change of targeted z-scores for a particular

metric.

In summary then, in the normalization model of QNF, when the QEEG data show

excessive deviations of z-scores, and those deviations correspond to the clinical picture,

the NF protocol is targeted to train the amplitude of the frequency in the direction of the

mean (i.e. create more or less energy within a specified frequency band). In other words,

if the QEEG indicates an excess of a beta frequency (i.e. high z-scores), and the

presenting symptoms are expected with that pattern (i.e. anxiety), the protocol would be

designed to decrease the amplitude of that beta frequency. Conversely, if the QEEG

indicates a deficit of an alpha frequency, with corresponding symptoms, the protocol

would be designed to increase the amplitude of the alpha frequency. The QNF model

38

then, is simply traditional amplitude based NF using the QEEG to guide the protocol

development for the NF sessions.

ZNF model of NF. The ZNF model leverages the statistical underpinnings of a

normal distribution, where a value converted to a z-score is a measure of the distance

from the mean of a population, such that the mean represents a range considered to be

normal (or typical) (Collura, 2014). With ZNF the real-time QEEG metrics are

incorporated into the NF session using a joint time frequency analysis (rather than fast

Fourier transform) to produce instantaneous z-scores, which allows for real-time QEEG

assessment to be paired with operant conditioning (Collura, 2014; Thatcher, 2013).

Therefore, where the QNF model has amplitude (as guided by the QEEG) as its targeted

metric, in its most basic form, the ZNF model targets the calculated real-time z-scores.

Yet, that being said, it is important to note that the z-scores can be considered a meta-

component of EEG metrics (i.e. amplitude or connectivity) and ultimately, even when z-

scores are targeted, the underlying EEG components are still being trained.

Nevertheless, directly targeting z-scores results in a different dynamic in the NF

training protocol. The goal is no longer to simply make more or less frequency amplitude,

but for the targeted excessive z-score metrics (whether high or low) to move towards the

mean, that is to normalize. Thus, there is a greater focus on the construct of

normalization. A second change is the inclusion of many more metrics to target. ZNF

makes available simultaneously, for up to ten frequency bands, both absolute and relative

power, ratios between frequencies (i.e. theta/beta ratio or alpha/beta ratio), as well as the

inclusion of connectivity metrics such as asymmetry, coherence, or phase lag, all as

active training metrics. Therefore, when applied to 4ZNF, the maximum number of

39

metrics to train is 248 (Collura, 2014) and, within the scope of the 19ZNF the maximum

number of metrics is 5700 (Collura, et al., 2009). These changes make the entire range of

all QEEG metrics, or a subset of selected metrics, available for targeting with ZNF

models. Moreover, the increased number of metrics targeted by 19ZNF may allow for an

increase in regulation and synchronization of neural activity simply by the greater

number of training variables. Nonetheless, one consistent theme remains aligned with the

QNF model, in that the decision to target normalization of QEEG metrics is determined

by the presenting clinical symptoms; thus when QEEG deviations correspond to

presenting symptoms, normalization is a reasonable treatment goal.

In asking if the 19ZNF improves attention, behavior, executive function, or

electrocortical function, the research questions for this study add to what is known

regarding whether operant conditioning with 19ZNF, produces clinical results that are

comparable to those reported in the literature for traditional or QNF models. Moreover,

this study also evaluates questions regarding 19ZNF and normalization of QEEG metrics.

This research fits within the overarching NF model with a specific focus on evaluating

efficacy of the ZNF model. As has been demonstrated in the literature, traditional NF is

well researched (Arns et al., 2009; Pigott et al., 2013), and as will be discussed in the next

section, the QNF model is well addressed in the literature. Conversely, as will be seen,

the ZNF models (4ZNF and 19ZNF) are still minimally represented in the literature.

Therefore, this study addresses an area which calls for further research.

Review of the Literature – Key Themes

QNF in the literature. Beginning with QNF models in reviewing the NF

literature is applicable in that the QNF model laid the ground-work for the ZNF models

40

that followed. Both QNF and ZNF models hold the generalized goal of normalizing the

QEEG, and for that reason, QNF is chosen as the first key theme in reviewing NF in the

literature. With few exceptions, literature presented on the QNF model comes from

research conducted in clinical settings. As a result, given the ethical constraints of

conducting research in clinical settings (e.g. asking clients to accept sham or placebo

conditions) (Gevensleben et al., 2012) few are blinded and/or randomized-controlled

studies.

Arns et al. (2012) conducted a well-designed open-label study of 21 ADHD

subjects using the QNF model, incorporating pre-post outcome measures and QEEG data.

The purpose was to investigate if the personalized medicine approach of QNF was more

efficacious (as defined by effect size) for ADHD than the traditional theta/beta or slow

cortical potential models, as reported in his meta-analysis three years earlier (Arns, et al.,

2009). The outcome measures incorporated were a self-report scale based on the

Diagnostic and Statistical Manual-IV list of symptoms and the Beck Depression

Inventory. The findings of this study were statistically significant improvements (p ≤

.003) in both the attention (ATT) and hyperactivity (HI) subtypes of ADHD symptoms as

well as depression symptoms. In this study, the mean number of sessions was 33.6, and

the effect size was 1.8 for the ATT subtype, and 1.2 for the HI subtype; this was a

substantial increase over the traditional model effect sizes of 1.0 (ATT) and 0.7 (HI)

respectively. This suggests the QNF model is more efficacious (i.e. effect size of clinical

improvements) than the older traditional theta/beta or slow cortical potential models.

Furthermore, in this study, non-z-score EEG microvolt data was reported for only nine

frontal and central region electrode sites, and three frequency bands, on a pre-post basis.

41

In addition to that the protocols employed are described as a selection of one of five

standard protocols, with QEEG informed modifications. The limitations of this study

were few but include a lack of a control group, a fairly small sample size, and that some

outcome measures were collected on only a sub-group of participants (thus reducing net

sample size). Moreover the pre-post QEEG data analysis was limited.

J. L. Koberda, Hillier, et al., (2012) reported on the use of QNF in a clinical

setting of a neurology private practice. All 25 participants were treated with at least 20

sessions of a single-channel traditional NF protocol, which was guided by QEEG data

and symptoms, with a goal to improve symptoms and normalize the QEEG. Clinical

improvement was measured by subjective reports from the participants in the categories

of not sure (n = 4), mild if any (n = 1), mild improvement (n = 3), improved/improvement

(n = 13), much improved (n = 2), and major improvement (n = 2); with a total of 84% (n

= 21) reporting some degree of improvement. QEEG change was reported as a clinical

subjective estimation (based on visual inspection of the QEEG topographic images) of

change in the targeted frequencies, in the categories of no major change/no improvement

(n = 6), mild improvement (n = 9), improvement (n = 8), or marked improvement (n = 1),

and one participant not interested in post-QEEG; with a total of 75% (n = 18) showing

estimation of improvement in the QEEG. Of note with this study was the heterogeneous

collection of symptoms treated which included ADD/ADHD, anxiety, autism spectrum,

behavior symptoms, cognitive symptoms, depression, fibromyalgia, headaches, major

traumatic brain injury, pain, seizures, stroke, and tremor, in varying degrees of

comorbidity per case. However, the primary limitation of this study was the loosely

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defined subjective estimations of improvement for both clinical symptoms and QEEG

outcomes.

In their randomized control study, Breteler et al. (2010) evaluated QNF as an

additional treatment with a linguistic education program. From the total sample of 19, ten

participants were in the NF group and nine were in the control group. Individual NF

protocols were based on QEEG results and four rules, with a generally (though not

strictly adhered to) 1.5 z-score cutoff; which resulted in the use of eight personalized

protocols. Improvement was determined by results of outcome measures of various

reading and spelling tests, as well as computerized neuropsychological tests. Paired t tests

were applied for analysis of the difference values between the pre and post scores. The

reported findings showed the NF group improved spelling scores with a very large

Cohen’s d effect size of 3; however no improvement in reading or neuropsychological

scores. QEEG data was reported, in terms of pre-post z-scores, on an individual basis (i.e.

per each case) for a limited number of targeted sites, frequencies, and coherence pairs;

with most showing statistically significant normalization.

In a retrospective study using archived clinical case files, Huang-Storms,

Bodenhamer-Davis, Davis, and Dunn (2006) evaluated the efficacy of QNF for 20

adopted children with a history of abuse who also had behavioral, emotional, social, and

cognitive problems. The children all received 30 sessions of NF (from a private practice

setting) with QNF protocols, which were individualized based on the QEEG profiles.

Data from the files of 20 subjects were collected to include pre and post scores for

outcome measures from a behavioral rating scale (Child Behavior Checklist; CBCL), and

a computerized performance test (Test of Variables of Attention; TOVA). The findings

43

for the CBCL were statistically significant (p < .05) for most scales and the TOVA

findings were statistically significant (p < .05) for three scales, thus demonstrating QNF

efficacy for the subjects in this study. There was no quantified QEEG reported; only

observations of general trends in the pretreatment QEEG findings, such as excess slow

waves in frontal and/or central areas.

Two researchers are most notable for several published studies evaluating the

QNF model, that being Walker and then Surmeli and colleagues. Each has a particular

consistent style in structuring their studies; and both have reported on the use of QNF

with a wide variety of clinical conditions. Therefore their works will be reviewed in a

grouping format. Walker has reported on mild closed head injury (Walker, Norman, &

Weber, 2002), anxiety associated with posttraumatic stress (Walker, 2009), migraine

headaches (Walker, 2011), enuresis (Walker, 2012a), dysgraphia (Walker, 2012b), and

anger control disorder (Walker, 2013). His QNF protocol development centers on

tailoring the protocol to the individual clinical QEEG data, with some restrictions of

either increasing or decreasing the amplitude of certain frequency ranges. For example,

the protocols for the anger outburst study restricted the target range to decrease only

excess z-scores of beta frequencies, combined with decreasing excess z-scores of 1-10 Hz

frequencies. For the migraine and anxiety/posttraumatic stress studies both were based on

individual excess z-score values found in the beta frequencies in a range of 21-30 Hz (to

decrease) with an addition of increasing 10 Hz. For all studies the electrode sites selected

were ones where the deviant z-scores in the targeted range were found. In the mild closed

head injury article, the protocol was different because the study was meant to evaluate

coherence training with a stated goal to normalize coherence z-scores. Thus, the most

44

deviant coherence pair was selected first (for five sessions each) and, then progressed to

lesser deviant pairs until the symptoms resolved or until 40 sessions were completed.

None of Walker’s reports declare a particular research design; still all involve pretest-

posttest comparisons of various outcome measures.

The outcome measures that Walker typically employs are primarily Likert or

percentage-based self-reports, except in the anger control disorder study where the

DeFoore Anger Scale self-report instrument was used to track the number of anger

outbursts. However, while all protocols are personalized, and based on QEEG findings,

there are no quantified pre-post QEEG data used as an outcome measure, and none are

reported in his studies. Overall the findings of all of Walker’s studies show improvements

in the targeted clinical conditions. In the mild closed head injury study, with an n = 26,

84% of the participants reported greater than 50% improvement in symptoms. For the

anxiety/post-traumatic stress article, with an n = 19, all improved on a Likert scale (1 –

10; 10 being worst) from an average rating of 6 before NF treatment to an average rating

of 1 after NF treatment. With the migraine study, where 46 NF participants were

compared to 25 patients who chose to remain on medication, 54% had complete

remission of headaches, 39% had a greater than 50% reduction, and 4% experienced less

than 50% reduction in migraines, all in the NF group, while in the medication group, 84%

had no change in migraines and only 8% had a greater than 50% reduction in headaches.

In three of his more recent studies, for the enuresis (n = 11), dysgraphia (n = 24), and

anger control research (n = 46), Walker reported all findings for all participants (in all

three studies) showed statistically significant improvement at p < .001.

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Surmeli and colleagues reported on Down syndrome (Surmeli & Ertem, 2007),

personality disorders (Surmeli & Ertem, 2009), mental retardation (Surmeli & Ertem,

2010), obsessive compulsive disorder (Surmeli & Ertem, 2011), and schizophrenia

(Surmeli et al., 2012). Notable in this collection of work are conditions previously not

known to respond to NF, such as personality disorders, mental retardation, Down

syndrome, and schizophrenia. All of these studies report the QNF protocol as being

individualized, as informed by a combination of the QEEG findings and clinical

judgment; with an overall goal to normalize the QEEG patterns. Notable for most of

Surmeli et al. studies are a high number of sessions reported for the cases; ranging from

an average of 50 to an average of 120 sessions. No particular research design is declared

in the Surmeli et al. studies, but here too, comparisons of pretest-posttest outcome

measures are reported.

The outcome measures in the studies mentioned above generally make use of

clinical assessment instruments designed to measure the symptoms targeted for the QNF

treatment. For example, the schizophrenia study employed the Positive and Negative

Syndrome Scale (PANSS), and for the obsessive compulsive disorder research they

incorporated the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). For many studies,

the computerized performance Test of Variable Attention (TOVA) was used. Yet, as with

Walker’s work, in spite of all protocols being individually QEEG-guided, QEEG data is

not used or reported as an outcome measure; only observations of general trends of the

changes in QEEGs are discussed. However, the targeted clinical symptoms, as measured

by the clinical assessments, were reported as having statistically significant improvement

in all studies. For the personality disorder study, with an n = 13, twelve were significantly

46

improved on all outcome measures; with the Symptom Assessment 45 Questionnaire at p

= .002, the Minnesota Multiphasic Personality Inventory (MMPI) Psychopathy scale at p

= .000, and the TOVA at p < .05 on the visual and auditory impulsivity scales. With the

article reporting the study with mentally retarded participants, including an n = 23, for 19

there was improvement on the Wechsler Intelligence Scale for Children-Revised (Verbal

scale, p = .034; Performance scale, p = .000; Total scale, p = .000) and the TOVA

(Auditory and Visual Omission scale, p < .02; Auditory and Visual Commission scale, p

< .03; Auditory and Visual Response Time Variability scale, p < .03). In the Down

syndrome study, while the outcome measure was not a commercialized assessment, they

did develop a questionnaire formulated to evaluate symptoms associated with Down

syndrome. The findings were that all subjects in the study (n = 7) showed improvement at

p < .02 on all questionnaire scales. With QNF for obsessive compulsive disorder, with an

n = 36, 33 showed improvement on the Y-BOCS (Obsession subscale, Compulsion

subscale, and Total score all p < .01). Finally, in the schizophrenia study, with an n = 51,

47 out of 48 patients who completed pre and post PANSS improved on all scales at p <

.01. Moreover of the 33 who were able to complete the MMPI, findings showed

significant improvements (p < .01) on the scales of Schizophrenia, Paranoia,

Psychopathic Deviation, and Depression.

This review of QNF research fits within this dissertation topic as examples of how

prior studies with QEEG data have been addressed in the literature. As can be seen,

studies evaluating QNF are typically found in clinical settings, with a wide variety of

clinical symptoms and/or mental health diagnoses, and frequently have relatively small

sample sizes. Moreover the NF protocols employed typically are tailored to the

47

individual, informed by QEEG, with a goal to normalize the QEEG. The overwhelming

majority of clinical QNF research employs retrospective pre-post comparison research

designs and the outcome measures used are tied to the symptoms of investigation. Yet

few, if any, report pre-post QEEG metrics, and only one (Arns et al., 2012) incorporated

statistical analysis of QEEG metrics as an outcome measure (and that was to a limited

degree). Therefore, in the QNF literature, it has become an accepted practice to define

efficacy in terms of measuring symptom improvement with various clinical assessments

(both commercially and informally developed). Nevertheless, clearly there is a gap in the

reporting of group QEEG z-score mean data in the present QNF research.

4ZNF in the literature. Given that 4ZNF is the forerunner to 19ZNF, this topic is

explored to provide historical context on both its development and its coverage in the

literature. While there are numerous studies in the literature for QNF, when it comes to

ZNF studies, such is not the case. However, for the 4ZNF model there are four

representations of 4ZNF clinical results in the literature.

In a first poster presentation on the topic, Wigton (2008) presented a single case

study where 4ZNF was used with an adult to address a diagnostic history of ADHD,

Bipolar disorder, and anxiety symptoms. The primary pre-post outcome measure was the

IVA. Also included were topographic images of pre and post QEEG assessments. After

25 sessions of 4ZNF, in addition to multiple subjective reports of symptom improvement

from the participant, the scaled scores for the IVA showed marked improvement. The full

scale Response Control scale improved from 29 to 94, and the full scale Attention scale

from 0 to 96. The QEEG findings (as reported by visual presentation of QEEG

topographic images) showed improvements in terms of normalization in the QEEG, most

48

noticeably in the left frontal delta and theta frequencies, as well as coherence and phase

lag normalization. However, a limitation of this study was a lack of statistical analysis of

pre-post QEEG data and the use of only one clinical assessment for outcome measures.

Collura et al. (2010) was the first peer-review publication addressing 4ZNF

although its organization was a loosely structured collection of clinical reports from six

clinicians covering 24 successful cases. Nonetheless, for a model with little scientific

evidence, it does stand as the only representation in the literature of a multiple-clinician

report of clinical results with 4ZNF. All cases reported clinical improvement, with no

abreactions, and the average number of sessions for all cases presented were 21.1. The

limitations of this case study are the lack of a structured methodology, no statistical

analysis, and limited pre-post outcome measures and/or QEEG data.

The study conducted by Hammer et al. (2011) represents, to-date, the only

quantitative analysis of 4ZNF. Its strength is a sound methodology with a randomized,

parallel group, single-blind design, together with QEEG z-scores as an outcome measure.

Though, the setting for this research was not in a clinical setting, but rather a university

psychophysiology laboratory wherein participants were recruited specifically for the

study. The purpose was to both explore 4ZNF as a new NF model, and to evaluate the

efficacy of two different 4ZNF protocols for insomnia. The primary findings suggest that

4ZNF may be a beneficial treatment for insomnia. While this study had very small group

sample sizes (n = 5 and n = 3) all insomnia related outcome measures resulted in pre-post

treatment improvement in symptoms, and normal (or near normal) sleep was achieved by

all participants. Moreover, at follow-up 6 to 9 months after treatment, over half sustained

the treatment response. The findings of this study included QEEG measures showing

49

statistically significant electrocortical change occurred for the delta frequency (p < .001)

and beta frequency (p < .01), but not high beta (p < .11). However, a limitation is that the

reported findings only included three frequencies, and the absolute and relative power z-

scores were combined in the analysis; therefore a more discrete picture of overall QEEG

normalization was not available. Further limitations of this study were the small sample

size and the lack of control group. Yet this study does stand alone, being a peer-reviewed

publication, as an example of a quantitative methodology for measuring normalization of

QEEG z-scores with the binomial test of significance, with the 4ZNF model.

A dissertation conducted by Lucido (2012) was a single case study to evaluate the

use of 4ZNF for an adult with Autism spectrum condition (ASC). This study used a

multiple baseline design, with five rounds of assessment data gathered before the 4ZNF

sessions, and a round of assessments at five incremental points during/after the NF

treatment. The outcome measures employed were the Neuropsych Questionnaire, the

CNS Vital Signs computerized neurocognitive assessment, and the Test of Nonverbal

Intelligence. While QEEG data was gathered and purported as an outcome measure, only

limited pre-post colorized topographic images were provided as a means to demonstrate

generalized changes in QEEG metrics. The results were that, with only one exception

(cognitive processing speed), all symptoms assessed with the outcome measures

improved. These included ASC symptoms, executive function, depression, anxiety, mood

stability, attention, and intelligence. To the study’s credit, this was a well-designed, well-

controlled case study; however still a representation of a single case, nonetheless.

Overall, the 4ZNF model is poorly represented in the NF literature. However,

there are still themes relevant to this dissertation. Of the studies reported, most are from

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clinical settings. Moreover, clinical assessments, as outcome measures, are used in all

studies. A particular stand out, though, is the Hammer et al. (2011) research, wherein

statistical analysis of QEEG metrics was used as an outcome measure.

19ZNF in the literature. With 19ZNF being the focus of this study, reviewing

what literature is available is necessary. Yet, there is an even greater dearth of published

literature for 19ZNF than 4ZNF. Therefore a review of conference oral and poster

presentations is necessary to sufficiently address what is known regarding 19ZNF.

Moreover, the literature reviewed herein is restricted to evaluative and/or case study

research reports regarding clinical applications of 19ZNF (rather than technical reviews

of 19ZNF).

In a first published clinical review of 19ZNF, Wigton (2009) reported initial

findings in which substantial QEEG normalization and clinical improvement was

achieved in as little as three sessions. While research into this technique was clearly

needed, the degree of success achieved in just a few sessions was a novel finding for

previously known NF models. Later in a conference presentation, Wigton (2010a)

reported on a series of case reviews that employed the Laplacian montage with 19ZNF.

There were 10 cases which included conditions such as anger issues, anxiety, ADHD, and

impaired cognition. The findings were that 19ZNF led to clinical improvements and

QEEG normalization, in less than 10 sessions, in seven out of the 10 cases. In this

presentation outcome measures included the IVA, the DSMD, and Likert scale reports. A

year later Rutter (2011) described, in a conference presentation, her use of 19ZNF and

how she was able to see initial indications of QEEG normalization in as little as five

sessions.

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In their conference oral presentation, J. L. Koberda, et al. (2012a) reported on a

comparison between 25 cases using traditional 1-channel NF and a mixed pool of 15

cases using either surface 19ZNF or LORETA ZNF. However, it is not clear how many

were 19ZNF and how many were LORETA ZNF cases. In this presentation the clinical

symptoms addressed in the 15 cases was varied and included anxiety, headaches, chronic

pain, cognitive and behavioral disorders, as well as focal neurological disorders. The

essential finding of this presentation was that both the traditional single-channel NF and

the 19ZNF/LORETA ZNF lead to improvement in clinical symptoms and improvements

in QEEG measures, but the 19ZNF/LORETA ZNF did so in fewer sessions. The

traditional NF group showed subjective self-report improvements of 84% and an

improvement of 75% of QEEG improvements, whereas the 19ZNF/LORETA ZNF group

showed 95% subjective improvement and 62.5% improvement in QEEG measures.

However an operationalized definition of these improvements was not clearly described

or quantified; nor were there any follow-up data reported. Nevertheless, the number of

sessions for the traditional NF was at least 20, whereas the number for the

19ZNF/LORETA ZNF group was an average of nine sessions.

Hallman (2012) presents a qualitative style clinical review of a single case study,

of a child with fetal alcohol syndrome. The purpose of the article was to describe the case

wherein 80 sessions of 19ZNF resulted in unexpectedly remarkable symptom and

behavior improvements. Moreover, the topographic images of pre-post QEEG data also

showed almost complete normalization; still there was no quantified measurement or

statistical analysis of QEEG data. There also were only subjective parental reports and no

outcome measures to quantify degree of symptom improvement.

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J. L. Koberda et al. (2012b) also conducted a single case study, of a 23 year-old

male, for the purpose of reporting clinical outcomes using two types of 19ZNF (surface

and LORETA). After only 15 sessions, improvements in a cognitive assessment outcome

measure were achieved, still there were no inferential statistical analysis reported for the

pre-post outcome measures. Moreover, the use of two distinctly different 19ZNF

modalities (surface and LORETA ZNF) makes it hard to know if one better accounted for

the improvement over the other. Finally, while improvements in QEEG data were

reported, again no inferential statistical analyses of these improvements were presented.

Krigbaum and Wigton (2013) present findings for 10 cases with 19ZNF. This

study is notable in that it introduced a proposed methodology for statistically

demonstrating z-score progression towards the mean (i.e. z = 0), and an approach for

plotting individual learning curves as a result of 19ZNF. Additionally, cases in the study

included outcome measures such as the IVA, DSMD, BRIEF and Likert scale (reported

on a supplementary basis, with only an indication of improvement or not), and all

outcome measures showed improvement at case completion. Repeated measures analysis

of variance (rANOVA) and paired t tests supported all three research questions such that

the z-scores progressed towards the mean (rANOVA absolute power, p < .001; relative

power, p < .04; coherence, p < .001); the post z-scores were closer to the mean than the

pre z-scores (paired t test absolute power, p < .007; relative power, p < .05; coherence, p

< .03); and clinical improvement was reported in all cases. However, no follow-up data

was reported.

Clearly, the research evaluating 19ZNF is in its infancy and there is a great need

for scientifically sound investigations. More so, the research needs to move beyond

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clinical reviews and case studies. As is incorporated in QNF research, use of clinical

assessments as outcome measures are important elements; additionally, finding ways to

include QEEG metrics as outcome measures would benefit 19ZNF research.

Outcome measures for ZNF research. This topic is included to explore outcome

measures that are suitable for ZNF research. A good deal of NF research occurs in

clinical settings, where assessment instruments are employed as part of the case workup.

As such, the use of those same measures after treatment is a natural fit for what are

frequently pretest-posttest research frameworks. Other than informal self-reports (i.e.

Likert scales) two types of popular outcome measures found in the NF literature are

rating-scale type assessments and computerized performance tests. Moreover, commonly

found in NF studies is the use of multiple outcome measures. Further, while the use of

EEG metrics as outcome measures of electrocortical change are infrequently incorporated

in NF research, there are a few reports in the literature which will be reviewed.

Computerized performance tests. Computerized performance tests are common

outcome measures in NF research, usually as a means to evaluate attention-related

symptoms associated with ADHD. One of those instruments is the IVA. While the IVA

was designed as a diagnostic aid for ADHD, the manual provides usage indications to

include assessing self-control and attention problems related to other disorders such as

depression, anxiety, head injuries, dementia, and other medical problems (Sanford &

Turner, 2009). Several NF studies have incorporated the IVA as an outcome measure to

assess attention related symptoms.

In their study to evaluate NF in a nonclinical group of college students’ cognitive

abilities, Fritson, Wadkins, Gerdes, and Hof (2008) used the IVA as one of their outcome

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measures; each group (experimental and control) had an n = 16. The stated objective was

to determine effects of NF on attention, impulsivity, mood, intellectual functioning,

emotional intelligence, and general self-efficacy. The IVA was one of several outcome

measures and was included to assess response control (i.e. impulsivity) and attention. The

researchers reported results in terms of the means and standard deviations of pre-post

values of eight of the primary scales of the instrument. The statistical analysis performed

were multivariate analysis of variance (MANOVA) between the control and experimental

groups.

In evaluating the utility of the Tower of London test, as a suitable assessment

instrument for clients with Asperger’s who undergo NF, Knezevic, Thompson, and

Thompson (2010) employed the IVA as one of the outcome measures. They included six

scales of the IVA (Auditory and Visual Prudence, Auditory and Visual Vigilance, and

Auditory and Visual Speed) to assess the efficacy of NF, and evaluate the measure of

impulse control as compared to the Tower of London test. The number of subjects

reported for the IVA varied for the different scales used from a low of n = 6 to a high of n

= 12, because they only included for analysis cases where pre-test scores needed to

improve. The researchers reported the means and standard deviations of the pre-post

values of the included scales, and performed paired t tests for statistical analysis.

Steiner, Sheldrick, Gotthelf, and Perrin (2011) conducted a randomized controlled

study with 41 children, comparing NF to a standardized computer attention training

program and used four outcome measures including the IVA. However, they only

included for analysis the two most broadly defined full-scale components of Response

Control and Attention, and only reported on an n = 6 for the NF group, and an n = 10 for

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the computerized training group. Repeated measures ANOVAs were performed to

analyze the pre-post outcome measures in this study.

Rating scales. Rating scale instruments are one of the most common assessment

tools found in NF literature for measuring clinical outcomes. Rating scales are

instruments which require rated objects to be assigned to categories or numerical

continua, by the rater or observer, based on their perception or remembrance of the

behavior being rated (Kerlinger & Lee, 2000). Rating scales frequently employed in NF

literature include the BRIEF, the Conner’s’ Rating Scale-revised (CRS-R), the Behavior

Assessment Scale for Children (BASC), and the Child Behavior Checklist (CBCL). The

following are examples from the literature of their use in NF studies.

In a randomized study, Orgim and Kestad (2013) compared NF to medication for

a heterogeneous ADHD group with various comorbidities; each group had an n = 16, and

the NF group was administered 30 NF sessions. The outcome measures included the

rating scales of CRS-R and BRIEF. They conducted analysis of covariance (ANCOVA)

statistical tests, using baseline measurement (Time-1) as the covariate; and they analyzed

group differences at Time-2 for selected scaled scores.

The study of Huang-Storms et al. (2006) provided an example of the use of rating

scales, in a retrospective clinical study, in the form of the CBCL together with a

computerized performance test. The total number of valid CBCLs reported on was an n =

18, and all aforementioned scales were included in the analysis. The statistics employed

were two-tailed paired t test analysis.

Drechsler et al. (2007) conducted a study with an experimental design to assess

the efficacy of slow cortical potential NF with ADHD using multiple outcome measures;

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where the experimental group had an n = 17 and the control group had an n = 13. Here

they employed two rating scales: The CPS-R and the BRIEF. Moreover, they only

included the composite or global scales from these instruments and performed repeated

measures MANOVAs for analysis.

In a randomized control study, Steiner et al. (2011) compared traditional NF to

computerized attention training to a waitlist control group; the group sizes were n = 9, n =

11, and n = 15. In this study, they used three rating scales: the CRS-R, the BASC, and the

BRIEF. Here too, they included selected scales from the assessments for analysis. The

statistics applied were rANOVAs, in an effort to detect if the experimental conditions

resulted in greater effects for the post NF assessment over the control group.

QEEG z-scores. As has been stated, with the QNF studies, by far, the vast

majority did not use pre-post EEG metrics or z-scores as an outcome measure. Though,

equally so, few traditional NF studies included EEG values as an outcome measure. Yet,

in one study purported to evaluate EEG effects of NF, Gevensleben et al. (2009) reported

values, as grouped together for nine regions across the scalp, and four frequency bands.

The averages of the microvolt values (raw, non z-score EEG values) were computed for

each region and frequency band, and post values minus pre values were used as a

measure of change. Since this was a study for traditional/amplitude NF, no z-score

metrics were used. Further, there were no goals of normalization in the NF protocols.

Two QNF studies do stand out for reporting, to some degree, pre-post EEG

metrics as part of the research. With Arns et al. (2012), non z-score pre-post EEG

microvolt data was analyzed, but for only nine sites, exclusive to frontal and central

areas, and for just three power frequencies. The group data was averaged, and presented

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in a graph, for each site and frequency combination. Statistically significant pre-post

differences were noted for this data. The second QNF study (Breteler et al., 2010), did

report some pre-post z-scores information, but it was lacking in depth. The QEEG data

were reported for a limited number of sites and frequencies, as well as coherence pairs,

presumably as identified from the personalized training protocols.

Hammer et al. (2011) presented a unique offering in performing the binomial test

of significance to evaluate z-scores as an outcome measure of normalization. While the

results did show a statistically significant number of z-scores normalized after 4ZNF, the

findings were for only three frequencies (delta, beta, and high beta), and combined values

for absolute and relative power. Moreover, this methodology is limited in that it only

provides a yes/no level of analysis for normalization, not a discrete measure of change

towards the mean. Nonetheless, it is a useful offering in an effort to present a measure of

normalization of the QEEG in response to 4ZNF.

One reason for the lack of reporting of z-scores as outcome measures may be due

to the nature of z-scores encompassing both positive and negative values, which, when

averaged, tend to cancel out a magnitude of effect. This was noted in Ramezani’s (2008)

dissertation, which was a study comparing pre and post z-scores of coherence and phase

lag as a result of traditional NF. He noted that mean comparisons of z-scores, with both

positive and negative values being cancelled in the averaging process, had the potential of

masking true differences. In an effort to account for this, he chose to transform the values

by computing the absolute value of the z-score. He then used a score of z ≥ 1.0 as

inclusion criteria for analysis. This approach allowed for statistical analysis, (i.e.

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averaging, ANOVAs, t tests) to be performed on the resulting z-scores transformed to

absolute values.

Krigbaum and Wigton (2013) presented a methodological approach to account for

positive and negative z-scores, by splitting the positive from negative z-scores, outside of

a cut-off score of ± z = 1.0, to calculate what is termed Sites of Interest (SoI). The

averaged SoI values were then plotted to display a learning curve for each participant,

and statistical analysis (i.e. t tests and rANOVAs) performed on the mean SoI z-score

values. While this methodology fits well for a single-subject design, and in quantifying

the progression of the z-scores towards the mean, its limitation lies in that (in the form

presented) it is not well suited for comparisons of group mean QEEG data. For example,

the split of positive and negative z-scores does not provide a single overall measure of

change for the z-scores. However, there is room to build on this research to develop a

methodology for comparing group data of QEEG z-scores.

Therefore, while few NF studies include EEG or QEEG z-score metrics as

outcome measures, when they do, frequently they only analyze selective components (i.e.

selected sites and/or frequencies). As a result, to date, no proposed methodology for

quantifying overall normalization has been published. Averaging non-transformed z-

scores is less than optimal due to the cancelling factor of the positive and negative values;

and the binomial test of significance provides only limited categorical analysis of the

data, without a measure of distance from the mean. The Krigbaum and Wigton (2013)

study appeared the closest to providing a model for measuring overall normalization of

the QEEG at this time. Still, building on this approach, by taking the absolute value of the

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z-scores, to provide a single value as a measure of the distance from the mean, could

prove advantageous.

In summary, common themes in the literature present suitable outcome measures

for NF research to consist of computerized performance tests, rating scale instruments,

and QEEG metrics. Examples such as the IVA, the BRIEF, and z-scores were discussed.

These findings are relevant to this research in that the same or similar instruments were

used for the present study.

Summary

In reviewing the 40 year history of NF, a discussion of the historical context of

EEG, QEEG, and NF was presented. NF is grounded in learning theory and through the

years various models, such as traditional NF, QNF, ZNF, have emerged. While 19ZNF is

one of the newest NF models, it does not enjoy a demonstration of efficacy by evidence-

based research, which exists for the traditional models. In fact, there are significant gaps

in the literature in that no scientifically rigorous studies of 19ZNF have been found. This

study aims to address this empirical gap by analyzing the question of efficacy of 19ZNF

in a clinical setting, thus contributing to the field in terms of beginning to fill this

empirical gap. Thus this study aims to contribute to the body of scholarly knowledge

regarding 19ZNF.

Prior QNF and ZNF research is commonly found in clinical settings. These

research studies typically employ pretest-posttest designs using relatively small sample

sizes, while incorporating clinical assessment instruments and occasionally QEEG

metrics, as outcome measures. Moreover, NF protocols are generally individually

tailored, based on QEEG findings, with a goal to normalize the QEEG; and

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heterogeneous collections of conditions included in studies is frequently found. These

traditions were followed for this study, in both design and outcome measures, in

evaluating 19ZNF. In utilizing QEEG z-scores as an outcome measure, prior research

methods (SoI and taking absolute values of z-scores) were expanded on to establish a

measure of distance from the mean, for statistical analysis of group data. The ZNF

theory, grounded in the use of real-time z-scores with a goal of normalizing the QEEG,

such that the z-scores move towards the mean (z = 0), underlies the 19ZNF approach;

which was the focus of investigation in this pretest-posttest comparison research.

A detailed review and description of the methodology for this research is

presented in the following chapter. To be included is an overview of the study, as well as

further discussion of data collection and analysis methods. Additionally, the

instrumentation, together with reliability and validity issues, will be discussed as it

applies to the study. Limitations will also be reviewed.

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Chapter 3: Methodology

Introduction

Over the years, new models of NF have been developed, and one of the most

current is 19ZNF. To-date, case study and anecdotal clinical reports within the field

indicate this new 19ZNF approach is an improvement over traditional NF models (J. L.

Koberda et al., 2012a; Wigton, 2013). Still, the efficacy of this new model has not yet

been established from empirical studies.

This research is different from other 19ZNF studies. It is a quantitative analysis of

pre-post outcome measures, with group data from a clinical setting, and thus, it is a

beginning in establishing empirical evidence regarding 19ZNF. The purpose of this

retrospective one-group pretest-posttest research was to compare the difference between

pre and post clinical assessments and QEEG z-scores data, before and after 19ZNF

sessions, from archived data of a private neurofeedback practice in the Southwest region

of the United States.

The remainder of this chapter reviews the problem statement and research

questions, discusses the methodology and research design, and also describes the

population and sample selection. Next, the instrumentation is presented together with a

discussion of the associated validity and reliability. Then, data collection and data

analysis is covered. Finally, a discussion of ethical considerations and the study

limitations are presented.

Statement of the Problem

It is not known, by way of statistical evaluation of either clinical assessments or

QEEG z-scores, if 19ZNF is an effective NF technique. This is an important problem

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because 19ZNF is a new NF model currently in use by a growing number of practitioners,

yet scientific research investigating its efficacy is lacking. Anecdotal reports are

insufficient as a basis for determining treatment efficacy and uncontrolled case studies

are scientifically weak (La Vaque et al., 2002). Therefore, scientifically sound evidence

of efficacy for 19ZNF is needed.

Research Questions and Hypotheses

For this research, the independent variable was the 19ZNF and the dependent

variables were clinical outcomes, as measured by the scaled scores from three clinical

assessments (IVA, DSMD, BRIEF) and z-scores from QEEG data. Given the

retrospective nature of this study, the approach for data collection was gathering archived

de-identified data, from closed case files, of a NF private practice. The process consisted

of collecting the necessary data elements (i.e. subject demographics, assessment scales

scores, and z-scores) into spreadsheets, for further analysis by statistical software (SPSS).

As will be discussed in detail in the research design section below, this study employed a

one-group pretest-posttest design. This was the best design for the proposed research

because the goal was to compare the means of the outcome measures at two different

time points (before and after 19ZNF) (Kerlinger & Lee, 2000).

As will be detailed in the instrumentation section, and briefly reviewed below, the

clinical assessments are generally designed to measure symptom severity of attention,

behavior, and executive functioning; and the z-scores are a representational measure of

electrocortical function. The clinical assessments are commercially available instruments,

widely used in the mental health field for measuring symptom severity. The QEEG data

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has been collected with a commercially available QEEG software package, which has

been in general use in the neurofeedback field since 2002.

The instrument to measure attention was the IVA continuous performance test.

This is a computerized test designed to assess both auditory and visual attention and

impulse control symptoms associated with ADHD (Sanford & Turner, 2009). The

associated research question and hypothesis was:

R1a. Does 19ZNF improve attention as measured by the IVA assessment?

Ha1a: The post scores will be higher than the pre scores for the IVA

assessment.

H01a: The post scores will be lower than, or not significantly different

from, the pre scores of the IVA assessment.

The instrument to measure behavior was the DSMD. This is a behavioral rating

scale, completed by parents, designed to assess behavior problems and psychopathology

in children and adolescents (Cooper, 2001). The associated research question and

hypothesis was:

R1b. Does 19ZNF improve behavior as measured by the DSMD assessment?

Ha1b: The post scores will be lower than the pre scores for the DSMD

assessment.

H01b: The post scores will be higher than, or not significantly different

from, the pre scores of the DSMD assessment.

The instrument to measure executive function was the BRIEF. This is a rating

scale, completed by parents, or self-rated in adults, design to measure observations of

executive function skills in everyday environments (Gioia, Isquith, Guy, & Kenworthy,

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2000; Roth, Isquith, & Gioia, 2005). The associated research question and hypothesis

was:

R1c. Does 19ZNF improve executive function as measured by the BRIEF

assessment?

Ha1c: The post scores will be lower than the pre scores for the BRIEF

assessment.

H01c: The post scores will be higher than, or not significantly different

from, the pre scores of the BRIEF assessment.

The instrument to measure the QEEG z-scores, which is a representational

measure of electrocortical function, was the QEEG assessments collected using the

Neuroguide software. This is software designed to provide statistical analysis of the

quantified EEG metrics, such that z-scores are calculated to allow a comparison to a

normative database (Thatcher, 2012). The associated research question and hypothesis

was:

R2. Does 19ZNF improve electrocortical function as measured by QEEG z-

scores such that the post z-scores are closer to the mean than pre z-scores?

Ha2: The post z-scores will be closer to the mean than the pre z-scores.

H02: The post z-scores will be farther from the mean, or not significantly

different from, the pre z-scores.

Research Methodology

The field of clinical psychophysiology makes use of quantifiable variables and the

associated research should include specific independent variables, as well as dependent

variables, which relate to treatment response (i.e. clinical assessments) and the measured

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physiological component (i.e. EEG metrics) (La Vaque et al., 2002). Yet, many NF

studies do not use the EEG metrics as a measure of the cortical component of

psychophysiologic function (Arns et al., 2009), but rather provide reports, which are

more qualitative in nature to discuss NF related QEEG changes. Moreover, NF research

needs to include quantitative methodologies, using QEEG data as an outcome measure, to

learn more about the psychophysiological basis of NF (Gevensleben, 2009). Therefore, a

quantitative methodology was selected, as opposed to qualitative, to address this need.

Currently, the available 19ZNF studies are in the form of qualitative research

(Hallman, 2012; J. L. Koberda et al., 2012a). This literature entails presenting data from

single case studies in the form of unstructured subjective reports of symptom

improvement, as well as graphical images of before and after QEEG findings, where the

improvement is represented by a change in color on the picture (without statistical

analysis of data). However, for this dissertation, the goal was to explore statistical

relationships between the variables under investigation; thus calling for a quantitative

approach. The strength of quantitative methodologies, including quasi-experimental

research, is that they provide sufficient information, regarding the relationship, and the

level of significance, for the investigation variables, to enable the study of the effects of

the independent variable upon the dependent variable (Carr, 1994). Therefore employing

a quantitative method is intended to leverage this strength in the evaluation of 19ZNF.

Research Design

This quasi-experimental research used a retrospective, one-group pretest-posttest

design. When the goal of research is to measure a modification to a behavior pattern, or

internal process that is stable and likely unchangeable on its own, the one-group pretest-

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posttest design is appropriate (Hunter & Schmidt, 2004; Kerlinger, 1986). This type of

design answers the research questions by comparing the collected dependent variable

pretest measures to the posttest values for each subject; thus comparing the members of

the group to themselves, rather than to a control or comparison group (Kerlinger & Lee,

2000). Consequently, the group members become their own control; thus controlling for

and thereby reducing the potential for extraneous variation due to individual-to-individual

differences (Kerlinger & Lee, 2000). Moreover, the size of the treatment effect can be

estimated by analyzing the difference between the pretest to the posttest measures

(Reichardt, 2009). The rationale for this being a retrospective study is because the data

available for analysis is from pre-existing archived records, which frequently provides a

rich source of readily accessible data (Gearing et al., 2006). Therefore, the chosen design

for this investigation is the best to evaluate the pre-post outcome measures from a clinical

setting, as well as the identified research questions for this study.

As previously stated, the independent variable was the 19ZNF and the dependent

variables were the data from the three clinical assessments and QEEG files; as such, the

specific instruments used to collect the data were the IVA, DSMD, and BRIEF

psychometric tests, as well as the QEEG software. A sample group was formed for each

dependent variable outcome measure so as to form four groups for analysis. Therefore,

using a one-group pretest-posttest design with these identified groups is fitting.

Population and Sample Selection

When individuals seek NF services they must choose among a variety of NF

models. Yet the dearth of scientific literature regarding 19ZNF limits the information

available for that decision process. The identified population for this research was made

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up of those seeking NF services (both adults and children), or those who accessed NF

services. These individuals may have had an array of symptoms, which adversely affect

their daily functioning, most commonly in the areas of attention, behavior, and executive

function; they may also have been previously diagnosed related mental health disorders.

From the total population (those seeking, or already have, accessed NF services),

this particular study population was identified as all prior clients of the NF private

practice which provided the retrospective data. Given the retrospective nature of this

research, there was no active recruitment of subjects; thus sample selection was

determined by inclusion criteria from available pre-existing cases. The study sample,

then, were the cases which met the inclusion criteria of being a 19ZNF case, having both

a pre and post QEEG assessment, as well as either an IVA, or a DSMD, or a BRIEF

assessment, for both pre and post conditions. Moreover, given the sample consisted only

of pre-existing de-identified data, as will be further detailed below (Data Collection

section), there was no need for an informed consent process. For this research, the total

aggregate sample size was 21 subjects, which was then divided into three additional

outcome measures groups (IVA, DSMD, or BRIEF). The sample size for the IVA group

was 10, the DSMD group was 14, the BRIEF group was 12, and all 21 subjects had

QEEG data.

In a meta-analysis evaluating traditional NF, for ADHD, not using QEEG–

targeted specificity in the NF protocols, Arns et al. (2009) reported an average (averaged

for attention and hyperactivity symptoms) Hedge’s d effect size of 0.85 (0.3 as small, 0.5

as medium, and 0.8 as large). In a more recent NF study where the treatment was more

personalized and targeted with QNF, Arns et al. (2012) reported the average Hedge’s d

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effect size was found to be nearly double to 1.45 for the combined symptoms of attention

and hyperactivity. Arns et al. (2012) suggested these findings indicate the personalization

of treatment protocols, afforded by QNF, improves clinical outcomes. Given that 19ZNF

also incorporates personalized QEEG-informed treatment protocols, it is reasonable to

expect equivocal effect sizes with 19ZNF. Thus, in determining a needed sample size

using the G*Power3 software (Faul, Erdfelder, Lang & Buchner, 2007), for the reasons

cited by Arns et al. (2012), it would be reasonable to use a predicted effect size in the

range of 1.0 to 1.5. Using the more conservative effect size value of 1.0, with a one-tail

analysis, alpha level of .05, and a power level of 0.80, for repeated measures t tests, the

calculated needed minimum sample size is eight. Therefore, groups with a sample size of

10 or more are sufficient for the data analysis to be performed in this study.

Instrumentation

The type of archived data used was from the following instruments: One

computerized performance test (IVA), two rating scales (DSMD and BRIEF), and QEEG

z-scores (Neuroguide software). All clinical assessments are commercially available

validated instruments, having a history of common use in the mental health industry. The

QEEG software is also commercially available, and since 2002 has been used

internationally by NF clinicians, in university research settings, and military/veteran

institutions (Besenyei, et al. 2012; Thatcher, North, & Biver, 2005). All instruments were

completed as part of the pre and post assessment routines during the previously

completed NF treatment process. All treatments were provided by the researcher who is a

state Licensed Professional Counselor, a board certified Neurofeedback Therapist, and a

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certified QEEG Diplomate. Descriptions of each of the instruments are provided next,

with a discussion of validity and reliability in separate following sections.

IVA. As reported by Sanford and Turner (2009), the IVA is a 13-minute

computerized test, with 500 responding or inhibiting trials, normed for ages six to adult,

designed to assess both auditory and visual attention and impulse control; with the aim to

aid in the quantification of symptoms and diagnosis of ADHD. Yet, the manual provides

usage indications to include assessing attention and self-control problems related to other

disorders, such as depression, anxiety, head injuries, dementia, and other medical

problems. The test taker is given standardized instructions, from a computer digitized

voice file, that they will see or hear the numbers 1 or 2, and to click the mouse when they

see or hear the number 1, and to refrain from clicking if they see or hear the number 2.

There are two global full-scale composite scores of Full Scale Response Control

Quotient, and Full Scale Attention Quotient. Each full scale is broken into auditory and

visual scales. Auditory and visual primary scales for Response Control include Prudence

(impulse control), Consistency (response reliability), and Stamina (sustained attention

over time). Auditory and visual subscales for Attention include Vigilance (inattention),

Focus (mental processing variability), and Speed (reaction time). The test results are

reported in the form of quotient scores such that a score of ≤ 85 is indicative of clinical

significance. As a performance test, the IVA is completed directly by the subject.

DSMD. The DSMD is a behavior rating scale designed to assess behavior

problems and psychopathology in children and adolescents; the child form (ages 5 to 12)

and adolescent forms (ages 13 to 18) have 110 items which describe problem behaviors,

with a 65% overlap between the two forms (Cooper, 2001). The rater can be either a

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parent or teacher, with separate norms for each; in this research, only parent ratings are

used. Both versions have (1) a composite Externalizing scale made up of Conduct and

Attention (child)/Delinquency (adolescents), (2) a composite Internalizing scale made up

of Anxiety and Depression, (3) a composite Critical Pathology scale made up of Autism

and Acute Problems, and (4) a global Total scale (Peterson, 2001). The instrument scores

are expressed in T scores, with scores ≥ 60 indicating clinical significance, and can be

completed in about 15 minutes.

BRIEF / BRIEF-A. The BRIEF is a rating scale, with 86 items, designed to

sample observations of children’s (ages 5 to 18) executive function skills in everyday

natural settings, with forms suitable for completion by parents and teachers (Donders,

2002). For this study only the parent form was available. This instrument is intended to

assess behavioral, emotional, and metacognitive skills, which broadly encompass

executive skills, rather than measure behavior problems or psychopathology (Donders,

2002). The BRIEF-A is the adult version (ages 18 to 90), self-report form, with 75 items,

which is designed to assess the views of one’s own executive function skills (self-

regulation) in their everyday environment (Gioia et al., 2000). Both instruments have an

overall summary scale of Global Executive Composite (GEC), which is comprised of two

primary sub-scales of Behavioral Regulation Index (BRI) and Metacognition Index (MI).

The BRI is made up of clinical scales of Inhibit, Shift, and Emotional Control for both the

adult and child versions, with the BRIEF-A adding a scale of Self-Monitor to the

behavior regulatory clinical scales category. The MI, for both the BRIEF and BRIEF-A,

is made up of five clinical scales of Initiate, Working Memory, Plan/Organize,

Organization of Materials, and Monitor. Both assessments take approximately 15 minutes

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to complete; and scores are expressed in terms of T scores, with scores ≥ 65 indicating

clinical significance (Gioia et al., 2000; Roth et al., 2005).

Neuroguide and QEEG acquisition. The QEEG data was acquired and

processed with the Neuroguide software. This software is designed to collect

conventional EEG data, and then allow for simultaneous visual inspection of the raw

EEG waveforms together with statistical analysis of the quantified EEG metrics

(Thatcher, 2012). Software modules allow the EEG data to be compared to a lifespan

normative database. The database has been normed, for both eyes open and eyes closed

conditions, with 625 individuals from ages of 2 months to 82 years of age, with the

included subjects being screened for normalcy (normal intelligence, lack of pathology or

mental health disorders) through history, interviews, neuropsychological testing and other

evaluations (Thatcher, Walker, Biver, North, & Curtin, 2003). The amplifier used for the

EEG acquisition was the Brainmaster-Discovery 24E (Brainmaster Technologies, Inc,

Bedford, OH), with an A/D conversion of 24 bits resolution, a sampling rate of 256 Hz,

and input impedance of 1000GOhms. Impedance is the obstruction of flow of electrical

current when measuring non-direct current signals (Farley & Connolly, 2005).

EEG data was acquired and processed as has been described by Krigbaum and

Wigton (2013), using accepted standards of QEEG acquisition methods, thus ensuring

quality recordings. An electrode cap (Electro-Cap Inc; Eaton, OH) was used to place the

19 electrodes according to the International 10-20 System referenced to linked ears, with

Electro-Cap brand electro-conductive gel. Electrode impedances were adjusted to be

below 10k ohm for all electrodes and balanced. The digital format of the EEG recording

was with a low-pass filter of 50 Hz and a high-pass filter of 0.5 Hz. The pre and post

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EEG recordings were acquired with eyes open in a waking-relaxed state, sitting in an

upright relaxed position. The instructions given were to remain still, inhibit muscle

activity from forehead, neck, and jaws, as well as eye movements and blinks. Screening

of EEG was conducted carefully to exclude technical and biological artifacts. The EEG

Selection method (Thatcher, 2012) was used to eliminate artifacts prior to submitting the

EEG to a fast Fourier transformation (FFT) procedure. The remaining edited EEG

consisted of an average of 1 minute of data (30 2s epochs), thus ensuring a representative

sample of data verified by the split-half and test-retest values being ≥ .90. The digitally

filtered frequency bands, for surface potential metrics of absolute power, relative power,

and coherence, were as follows: Delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), alpha1

(8-10 Hz), alpha2 (10-12 Hz), beta (12-25 Hz), beta1 (12-15 Hz), beta2 (15-18 Hz), beta3

(18-25 Hz), and high beta (25-30 Hz).

Validity

The concept of test validity refers to degree to which it accurately measures that

which it proposes to measure, and also how well it measures the target in question

(Anastasi & Urbina 1997). Thus, the emphasis is on the accuracy of the measure with

regard to the aspect of what is to be measured. Aspects of validity of the outcome

measures for this study will next be addressed.

IVA. A concurrent and diagnostic validity study was conducted by Nova

Southeastern University and BrainTrain, Incorporated. The findings suggested the overall

accuracy, when compared to diagnoses of ADHD provided by physician/psychologists, to

be statistically significant (p < .0001). Moreover, the sensitivity (true positives) was

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reported to be 92%, specificity (true negatives) as 90%, and positive and negative

predictive power as 89% and 93% respectively (Sanford & Turner, 2009).

DSMD. Peterson (2001) reported content validity for the DSMD to be good, with

a strong congruence with the Diagnostic Statistical Manual-IV criteria regarding the

behaviors examined. Moreover, the DSMD scales have a diagnostic potential to identify

normal versus hospitalized children/adolescents with an accuracy range of 70% to 90%

(Cooper, 2001). In a study to examine concurrent validity with the BASC and the CBCL,

Smith and Reddy (2002) found the DSMD to demonstrate strong concurrent validity with

scales, which were conceptually similar. For example, between the DSMD and the

CBCL, the correlations were .81 for the Externalizing scale, .83 for the Internalizing

scale, and.86 for Total scale (Smith & Reddy, 2002). This is important, given that many

NF studies have previously used the BASC and CBCL; thus demonstrating the DSMD to

be similar to other rating scales, as a behavior measure, used with prior NF studies.

BRIEF / BRIEF-A. Content validity for the BRIEF was determined by seeking

agreement between multiple pediatric neuropsychologists and the test authors for fit of

each test item. The items retained in the clinical scales have item-total correlations that

range from .43 to .73 (Gioia et al., 2000). Content validity for the BRIEF-A was

conducted in a similar manner by seeking agreement among multiple neuropsychologists

experienced with executive function issues in clinical practice. Of the retained items for

the clinical scales the agreement ranged from .38 to .98 (Roth et al., 2005).

Neuroguide QEEG database. As described by Thatcher et al. (2003), the

validation procedure for the Neuroguide QEEG database included a leave one out

Gaussian (normal distribution) cross-validation process, whereby the data for each

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subject in the database was removed and then compared to that same database. This is

important because the database, which is being compared to needs to fit the normal curve

to ensure unbiased error estimates. In a normal distribution cross-validation with a perfect

fit, it would be expected that 2.3% of the comparison sample would fall outside of +2

standard deviations (SD) and again at -2 SD, and that 0.13% at +3 SD and again at –3

SD. Therefore, percentages which approximate these values can be deemed as validating

the normal distribution. The cross-validation process for the Neuroguide database

revealed an overall percentage (of all metrics) at +2 SD to be 2.58%, and for –2 SD to be

1.98%; then for +3 SD to be 0.18%, and for -3 SD to be 0.14%. Moreover, the kurtosis

and skewness of the database, if fitting the normal distribution, would be within a few

percentage points of zero. Thatcher, Walker et al. reported the validation process found

the Neuroguide database to meet the criteria for skewness with an overall percentage of

0.17%, and for kurtosis with an overall percentage of 2.91%.

Reliability

Reliability is an important aspect in determining if one can trust that a particular

assessment will give a comparatively similar measure if it is given at another time. As

such, reliability reflects score consistency and predicts how much variation one can

expect from one administration of the test to the next (Anastasi & Urbina, 1997). Thus,

reliability allows an estimate of the error of measurement for the instrument. Aspects of

reliability of the outcome measures for this study will next be addressed.

IVA. A test-retest study was conducted by Nova Southeastern University and

BrainTrain, Incorporated, with a testing interval of 1 to 4 weeks. The results showed

statistically significant (p < .01) reliability coefficients ranging from .37 to .75 (Attention

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scales: .66 to .75; Response Control scales: .37 to .41). The findings of this study are

further reported to support the IVA as being a stable measure of performance while also

being robust against learning or practice effects, such that changes in test scores over

time can reliably be attributed to environmental or treatment effects (Sanford & Turner,

2009).

DSMD. The test-retest reliability was measured for the DSMD and is reported to

range from .80 to .90 for the scales, with an interval of a 24-hour period (Peterson, 2001).

BRIEF/BRIEF-A. The test-retest reliability was measured for both the clinical

and normative samples of the BRIEF, which was reported to be .81 for the normative

sample and .79 for the clinical sample, with an average interval of two to three weeks;

whereas the reliability for the BRI, MI, and GEC was ≥ .80 for both the clinical and

normative samples (Gioia et al., 2000). For the BRIEF-A the test-retest reliability, over

an average interval of four weeks, for the clinical scales was reported to range from .82 to

.93; with the reliability for the BRI, MI and GEC being > .92 (Roth et al., 2005).

Neuroguide QEEG software. Recently, a study was conducted to evaluate the

reliability of the FFT metrics of the Neuroguide software. Cannon et al. (2012) found the

Neuroguide test-retest reliability, at a 30-day interval, to be ≥ .77 for absolute and relative

power, and coherence. A further measure of reliability, with the individual EEG records

in Neuroguide, are a test-retest and split-half measure which is calculated when the

artifacts are removed, which when ≥ .90 provide a representative sample of the overall

EEG record (Thatcher, 2012). The edited EEG records for this study were edited such

that both the split-half and test-retest measures were on average ≥ .90.

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Data Collection Procedures

The sample consisted of a convenience sample from reviewed closed cases, of

clients from a private neurofeedback practice, who were administered the clinical

assessments and QEEGs before and after 19ZNF treatment. Regarding the retrospective

data used in this study, those clients were informed that after their treatment was

completed and their case closed, non-identifying data could be used for quality assurance

and/or future research purposes; they were all given the opportunity to opt-out. To be

considered an available 19ZNF case, the clinical symptoms presented during the intake

assessment corresponded with the z-score deviations of the QEEG findings, such that a

treatment goal of overall QEEG normalization was clinically appropriate. While the

19ZNF protocol developed for each case was individually tailored to the clinical and

QEEG findings, and possibly modified at each session to correspond with the baseline

QEEG data of that day, the same treatment goal always applied; that of overall QEEG

normalization. Therefore, the underlying 19ZNF protocol of overall QEEG normalization

was consistent for all cases. The hardware platform was the Brainmaster Discovery 24E

amplifier, and the software platform was either the Brainmaster Discovery or Neuroguide

NF-1 19ZNF software. The 19ZNF sessions used the Brainmaster Flashgame visual NF

displays (i.e. simple non-movie animations); and the reward percentages were

approximately 30% to 50% (i.e. 20 to 30 rewards-per-minute).

As depicted in Figure 1.1, from the available 19ZNF cases, an initial group was

formed for which pre-post QEEG assessments existed, and for which either the IVA,

DSMD, or BRIEF pre-post assessment data were also available (n = 21). From this

collection, three additional groups were formed. One group was created for the IVA data

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(n = 10), a second group for the DSMD data (n = 14), and a third group for the BRIEF

data (n = 12).

The data collected for this study were from pre-existing documents/files and

recorded by the investigator in a manner such that the subjects cannot be identified.

Therefore, in accordance with 45 CFR 46.101(b) and 46.101(b)(4), this research was

exempt from the requirements of the Protection of Human Subjects 45 CFR part 46

(2009) regulation. Consequently, the university Institutional Review Board (IRB)

determined this study to be exempt from IRB review, under exemption category 7.4 (see

Appendix B). As such, IRB-approved informed consent for use of the de-identified data

for this research was not necessary. All data for this study were previously obtained

during the course of subjects’ NF treatment. While the data came from records that

already exist prior to the start of the study, there was a form of data collection by pulling

de-identified information from a review of the archived records of the private practice.

Upon IRB approval, the information was gathered and de-identified in a format such that

it was impossible to identify the subjects. For example, copies/scans were made of the

assessment scoring sheets, but names and/or birthdates (or any other identifying

information) were redacted, and only a sequential case number was assigned and written

on documents associated with that case. The pre and post scaled score data, from the

copied assessment forms, were entered into a spreadsheet to facilitate data analysis. For

the QEEG data, with the Neuroguide software, the report generation feature was used to

save the z-scores into tab delimited text files, which were then saved as Microsoft Excel

worksheet files, thus preparing the data for further analysis.

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The redacted paper forms of the collected data set are stored in a secured manner

(i.e. under lock/key) and separate from the clinical source files (which also provides for

physical backup of data). For data that was entered into spreadsheets and the statistical

software package, those digital files are stored on an external flash drive separate from

any installed computer hard-drive; and, when not in use, will be kept with the paper data

files in the same secured manner. The data are stored in a secured manner, with hard-

copy (i.e. paper) data as a form of permanent backup, separate from the archived source

files, and will be maintained for the required 3 years after the completion of the study. At

the end of the 3 years paper files will be shredded and electronic media digitally erased.

A further subject identity protection were that findings reported were only descriptive

group data, and no individual case was described or discussed; thus preventing any

possible inadvertent identification of persons.

Data Analysis Procedures

In general, the research questions asked if 19ZNF improved attention, behavior,

executive function, and electrocortical function, as measured by the clinical assessments

of the IVA, DSMD, BRIEF, and QEEG z-scores. All alternative hypotheses were similar

in that the IVA hypothesis predicted the post scores would be higher than the pre scores,

the DSMD and BRIEF hypotheses predicted the post scores would be lower than the pre

scores, with the z-score hypothesis predicted post z-scores to be closer to the mean than

pre z-scores. The null hypotheses all predicted no significant difference, or a difference

opposite the direction of improvement. The level of significance for this study was alpha

= .05.

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As previously described in the above section, the scaled scores from the clinical

assessments and QEEG z-scores were collected from archived clinical files and organized

by data entry into spreadsheets for analysis in SPSS v21 software. Columns for relevant

data categories (demographics, pre scores, post scores, difference scores), and identified

relevant scales (composite and global), were created to facilitate entry of data into the

fields of the spreadsheets. The data analysis started with performing descriptive statistics

on each of the sample groups; the means for the pre, post, and difference scores were also

calculated. The specific scales that were analyzed from each clinical assessment are

described next, and are followed by details related to the z-score data analysis.

IVA. The IVA assessment has two primary categories of scales, Response

Control and Attention. The research question associated with this variable focused on

improvement of attention. Thus, in order to maintain alignment with the research

question, only the overall scales specific to attention were analyzed. Therefore scores

from the Full Scale Attention Quotient, Auditory Attention Quotient, and the Visual

Attention Quotient, were collected and analyzed. Additionally, these scales have higher

reliability measures than Response Control scales.

DSMD. The DSMD has two composite scales more specific to generalized

behavior, that being the Externalizing Composite and Internalizing Composite scales, as

well as a Total scale. These three scales correlate strongly (.81, .83, .86, respectively)

with similarly named scales from the CBCL, which is an instrument commonly used as

an outcome measure of behavior in NF studies. Thus this strategy maintained alignment

with the associated research question (improvement of behavior) for this variable.

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BRIEF / BRIEF-A. While all scales on the BRIEF instruments capture elements

of executive function, in order to maintain alignment with the analysis of the other

instruments (i.e. analyzing generalized composite/global scales), only the composite

scales of Behavior Regulation Index and Metacognition Index, as well as the Global

Executive Composite scale were analyzed. Moreover, both the BRIEF and BRIEF-A

contain these composite/global scales, thus maintaining consistency in the child and adult

assessment measures. Therefore these scales maintained alignment with the associate

research question (improvement of executive function) for this variable.

QEEG z-scores. The QEEG z-scores are a representational measure of

electrocortical function, such that z-scores which are closer to the mean represent

improved functioning; thus maintaining alignment with the research question associated

with this variable. The z-score data were calculated for the QEEG metrics of absolute

power, relative power, and coherence; the same procedure was followed for each metric.

First the z-scores were converted into a spreadsheet format. Next, the values were

transformed to the absolute value. Then, the pre z-scores which were ≥ 1.0 were

highlighted as being the targeted (by site and frequency) z-scores. Those targeted z-

scores were averaged to create a single value, representing an overall distance from the

mean for that metric, for that case. Next, the same targeted z-scores for the corresponding

post values (i.e. same site and frequency) were identified and averaged. This allowed the

pre and post averaged targeted z-score values to be compared, as a measure of change,

such that a lower post value (compared to the pre value) would be closer to the mean.

Statistical analysis. Given that each of the variables forms a separate analysis

group, the proposed data analysis aligned with the one-group pretest-posttest design. The

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paired (within-subjects/repeated measures) t test was appropriate (assuming the

difference scores to be normally distributed) for this quantitative research, with

continuous variables, because it was based on the difference scores (between pre and

post) for measures taken for each person in a single sample, while allowing for sufficient

statistical power with smaller sample sizes (Gravetter & Wallnau, 2010). Effect size was

computed for discussion of practical results, and compared to that previously reported

from prior studies in the literature.

The statistical analysis was conducted with the SPSS v21 statistical package. For

all hypotheses, the plan was for paired t tests on the pre/post difference scores, for the

means of the selected scales and z-scores, for each outcome measure. The data from the

spreadsheet columns, for the pre and post values (for the scales of each outcome

measure) was transferred into SPSS. Next, the SPSS command sequence selected was

Analysis>Compare Means>Paired Samples T Test. The pre values were identified as

Variable1 and the post values identified as Variable2, and the Confidence Interval

Percentage will be set at 95%. Finally, Hedge’s d effect sizes were calculated with the

Metawin 2.1 software.

Ethical Considerations

There were no ethical problems for this dissertation primarily because it was

determined to be exempt from the requirements of the Protection of Human Subjects

45 CFR part 46 (2009) regulation. Consequently, IRB-approved informed consent for

research was not necessary. As described above, all data was pre-existing prior to the

start of the study and recorded such that there was no potential for revealing the

identity of any person included. The researcher owned the private practice data

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therefore no data use or site authorization was needed. The data was stored in a

secured manner, with hard-copy (i.e. paper) data as a form of permanent backup,

separate from the archived source files, and will be maintained this way for the

required 3 years after the completion of the study. At the end of the 3 years, paper files

will be shredded and electronic media digitally erased.

Limitations

There were three primary limitations to this study; that of research design

elements, sample size, and the question of efficacy. Moreover, it is important to examine

potential sources for bias in any research. Thus, this aspect will also be discussed.

Most criticisms of pretest-posttest designs, which imply they are inadequate due

to threats to internal validity, can be traced back to Campbell and Stanley (1963).

However, as pointed out by Hunter and Schmidt (2004), the identified limiting elements

(history, maturation, instrumentation, testing, and regression) were only presented by

Campbell and Stanley as potential threats, which may or may not adversely impact a

study. Moreover, in studies of psychological factors, where the intent is intervention

evaluation, the behavior targeted by the treatment (i.e. the DV) is typically quite difficult

to change without some intervention; thus the Campbell and Stanley potential validity

threats were ruled out (Hunter & Schmidt, 2004).

Nonetheless, a general limitation of designs, which incorporate a pretest-posttest

framework is primarily related to the passage of time between administering the pre and

post assessments (Kerlinger & Lee, 2000). Factors such as history (concurrent events

external to the study scope) and maturation (internal growth factors occurring regardless

of interventions) cannot be controlled for. Therefore, it is not possible to know whether or

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not they have impacted the DV measures (Hunter & Schmidt, 2004). Yet, when the time

between testing points is short, the impact of extraneous variation is lessened (Kerlinger

& Lee, 2000; Reichardt, 2009). In this study, the time between the pre and post

assessment was relatively short, measured in terms of weeks. Therefore, the impact of

time-related confounds were considered to be minimal. Also, identified as a potential

validity threat is the phenomenon of a regression to the mean, where high or low scores

are, by chance, found to be closer to the mean when retested. However, there is an

inverse relationship between the degree of statistical regression and an instrument’s

reliability (Kirk, 2009); such that instruments with higher reliability have less variability

in the measurement error. Given the reliability of the instruments in this study are

relatively high, the estimate of the error of measurement is comparatively low. Thus,

potential validity threats related to regression effects were minimal.

Larger sample sizes are preferred in order to allow for stronger statistical analysis

and more generalizability (Gravetter & Wallnau, 2010). Given this study used pre-

existing archived data, the number of samples was restricted to what was found in the

files; thus there was no option to increase sample size. Though, as discussed, the sample

sizes for each group had sufficient power to allow for adequate statistical analysis.

In order to fully address the question of efficacy, additional studies involving both

follow-up data and control group comparison data are necessary. This is especially true in

answering whether 19ZNF is superior to other QEEG-based approaches. Therefore,

limitations of this study, which also must be recognized, are a lack of comparison to a

traditional NF group and a lack of a randomized control group. Nevertheless, given the

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data for this research comes from a real-world clinical setting, the findings of this study

can still contribute to advancing the scientific knowledge of 19ZNF.

Finally, in examining potential sources of bias, in a retrospective study where

the data comes from the archived treatment cases of the researcher, a question could

be asked regarding how the researcher can account for the potential. Given the data

was pre-existing in closed cases, and could only be reported, the numerical

information could not be changed nor manipulated. In other words, the data existed in

a set form, and the statistical analysis conveys the message. Moreover, by de-

identifying the data such that every subject was reduced to merely a case number, the

researcher even became blind to the identity to the subjects within the study. Further,

there was no qualitative data in this study for the researcher to interpret. For these

reasons, it is believed the potential for bias was minimized in this study.

Summary

In summary, the methodology for this retrospective pretest-posttest comparative

research was presented. As was reviewed, the independent variable was the 19ZNF and

the dependent variables were the data from the three clinical assessments and QEEG z-

scores; the instruments were the IVA, DMSD, BRIEF psychometric tests, and QEEG

software. The population was described as those who seek NF services, with the study

sample being the pre-existing data available meeting the inclusion criteria, such that four

groups were formed; one group for each outcome measure. A discussion was presented

regarding data aspects germane to a retrospective study, such as how the data was pre-

existing and only de-identified information was collected. Consequently this study

qualified as exempt from the requirements of the Protection of Human Subjects 45 CFR

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part 46 (2009) regulation, and IRB-approved informed consent was not necessary.

Finally, limitations were reviewed, and what are typically identified as potential

weaknesses in pretest-posttest designs (Campbell & Stanley, 1963), were minimally

impactful because intervention targeted behaviors frequently do not change without

effective intervention (Hunter & Schmidt, 2004), there was a short pre-post time interval

(Reichardt, 2009), and the instruments employed in this study have relatively high

reliability measures (Kirk, 2009).

In this study, all research questions were similar and the paired t test was an

appropriate statistic to compare the means of the different data groups. Moreover, effect

size was computed and compared to prior studies. In the following chapter, the process of

the data analysis, as well as results, will be discussed.

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Chapter 4: Data Analysis and Results

Introduction

Addressing efficacy of 19ZNF is important because it was not known, by way of

statistical evaluation of either clinical assessments or QEEG z-scores, if 19ZNF is an

effective NF technique. Therefore, the purpose of this quantitative research was to

evaluate 19ZNF, in a clinical setting, using a retrospective one-group pretest-posttest

research design. Generally, the research questions asked if 19ZNF improves attention,

behavior, executive function, and electrocortical function, as measured by the outcome

measures of the IVA, DSMD, BRIEF, and QEEG z-scores. All alternative hypotheses

were similar in that the IVA hypothesis predicted the post scores would be higher than

the pre scores, the DSMD and BRIEF hypotheses predicted the post scores would be

lower than the pre scores, and the QEEG hypothesis predicted post z-scores would be

closer to the mean than pre z-scores. The null hypotheses all predicted no significant

difference, or a difference opposite the direction of improvement.

This chapter first presents the descriptive data of each of the groups for the IVA,

DSMD, BRIEF, and QEEG z-score data. Then, the steps taken for data analysis are

described. Finally, results of the data analysis are presented.

Descriptive Data

The QEEG group represented the inclusion of all subjects for the study, from

which the other groups were formed; therefore, it is described first. Then, the groups for

the IVA, DSMD, and BRIEF are described. Table 4.1 summarizes the descriptive

information as discussed in the following sections. It is important to note, while the

clinical assessment groups were diverse diagnostically, when viewed by clinical

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complaints, in terms of the neuropsychological constructs of attention, behavior, or

executive function, the subjects collectively formed well-defined groups for which the

assessment instruments are designed to measure.

QEEG group. The total sample size for this group was 21; there was no reported

experience of 19ZNF prior to coming to this practice. The subjects ranged in age from 7

to 63 years, with a mean age of 21.19 years (SD = 18.12); including 15 children and six

adults, 10 males and 11 females. Seventeen of the subjects were White, two were Asian,

and two were Latino; while five were categorized as low socioeconomic status (SES), 14

as medium SES, and two as high SES. The make-up of the diagnosis 2 and/or presenting

conditions included mostly a combination of ADHD-Inattentive presentation (ADHD-I)

and ADHD-Combined presentation (ADHD-C) (ADHD-I = 4, ADHD-C = 7); yet, there

were three subjects with ADHD-C comorbid with another disorder (ADHD-

C/Unspecified Anxiety Disorder, ADHD-C/Autism Spectrum Disorder, ADHD-

C/Unspecified Learning Disorder). Finally, the other diagnoses included one comorbid

Unspecified Anxiety/Unspecified Depressive Disorder, one Autism Spectrum Disorder,

one Unspecified Bipolar Disorder, one Reactive Attachment Disorder, one comorbid

Obsessive-Compulsive Disorder/issues with executive function, and two subjects with

presenting issues of difficulty with executive functioning. A total of 16 subjects had no

medication usage, two subjects were on medication, two subjects started on medication

2 Given the retrospective nature of the data, all initial diagnoses were made in accordance with the

Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-lV-TR; American

Psychiatric Association, 2000). However, all diagnoses criteria were confirmed with, and are reported in

accordance with the DSM-5 (American Psychiatric Association, 2013) taxonomy.

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but had ceased medication by the post assessment, and one subject had a reduction of

medication by one-third at the time of post assessment. The number of sessions from pre

assessment to post assessment ranged from three to 20, with a mean of 10.90 (SD = 3.88).

The targeted session frequency was once per week. The number of weeks for treatment

(pre to post assessment) ranged from two to 22, with a mean of 11.76 (SD = 5.19).

Finally, the number of weeks from pre assessment to post assessment ranged from two to

43, with a mean of 15.10 (SD = 10.03). The descriptive data for this group is summarized

in Table 4.1.

IVA group. The total sample size for this group was 10. The subjects ranged in

age from 7 to 63 years, with a mean age of 26.80 years (SD = 19.84); including five

children and five adults, five males and five females. Nine of the subjects were White,

and one was Latino; while three were categorized as low SES, five as medium SES, and

two as high SES. The make-up of the diagnoses and/or presenting conditions included

mostly a combination of ADHD, with three ADHD-I and four ADHD-C; yet, there were

two subjects with ADHD-C comorbid with another disorder (ADHD-C/ Unspecified

Anxiety Disorder, ADHD-C/ Unspecified Learning Disorder). Finally, the other

diagnoses included one subject with presenting issues of difficulty with executive

functioning. A total of eight subjects had no medication usage, one subject was on

medication, and one subject started on medication but had ceased medication by the post

assessment. The number of sessions from pre assessment to post assessment ranged from

three to 15, with a mean of 9.70 (SD = 3.92). The targeted session frequency was once

per week. The number of weeks for treatment (pre to post assessment) ranged from two

to 15, with a mean of 9.40 (SD = 4.40). Finally, the number of weeks from pre

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assessment to post assessment ranged from two to 43, with a mean of 13.20 (SD = 11.11).

The descriptive data for this group is summarized in Table 4.1.

DSMD group. The total sample size for this group was 14. The subjects ranged in

age from 7 to 17 years, with a mean age of 10.86 years (SD = 2.91); including 14 children

and no adults, seven males and seven females. Ten of the subjects were White, two were

Asian, and two were Latino; while three were categorized as low SES, nine as medium

SES, and two as high SES. The make-up of the diagnoses and/or presenting conditions

included a combination of ADHD, with two ADHD-I and five ADHD-C; yet, there were

two subjects with ADHD-C comorbid with another disorder (ADHD-C/Autism Spectrum

Disorder, ADHD-C/Unspecified Learning Disorder). Finally, the other diagnoses

included one comorbid Unspecified Anxiety/Unspecified Depressive Disorder, one

Autism Spectrum Disorder, one Unspecified Bipolar Disorder, one Reactive Attachment

Disorder, and one subject with presenting issues of difficulty with executive functioning.

A total of 11 subjects had no medication usage, one subject was on medication, one

subject started on medication but had ceased medication by the post assessment, and one

subject had a reduction of medication by one-third at the time of post assessment. The

number of sessions from pre assessment to post assessment ranged from three to 20, with

a mean of 11.43 (SD = 4.13). The targeted session frequency was once per week. The

number of weeks for treatment (pre to post assessment) ranged from three to 22, with a

mean of 12.57 (SD = 5.60). Finally, the number of weeks from pre assessment to post

assessment ranged from six to 37, with a mean of 15.36 (SD = 8.63). The descriptive data

for this group is summarized in Table 4.1.

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BRIEF group. The total sample size for this group was 12. The subjects ranged

in age from 7 to 63 years, with a mean age of 20.25 years (SD = 19.97); including 10

children and two adults, six males and six females. Eleven of the subjects were White,

and one was Latino; while two were categorized as low SES, nine as medium SES, and

one as high SES. The make-up of the diagnoses and/or presenting conditions included a

combination of ADHD, with two ADHD-I and two ADHD-C; yet, there were two

subjects with ADHD-C comorbid with another disorder (ADHD-C/Autism Spectrum

Disorder, ADHD-C/ Unspecified Learning Disorder). Finally, the other diagnoses

included one comorbid Unspecified Anxiety/Unspecified Depressive Disorder, one

Autism Spectrum Disorder, one Reactive Attachment Disorder, one comorbid Obsessive-

Compulsive Disorder and issues with executive function, and two subjects with

presenting issues of difficulty with executive functioning. A total of 10 subjects had no

medication usage, one subject was on medication, and one subject started on medication

but had ceased medication by the post assessment. The number of sessions from pre

assessment to post assessment ranged from three to 20, with a mean of 11.83 (SD = 2.69).

The targeted session frequency was once per week. The number of weeks for treatment

(pre to post assessment) ranged from approximately three to 22, with a mean of 13.50

(SD = 3.97). Finally, the number of weeks from pre assessment to post assessment ranged

from six to 37, with a mean of 16.17 (SD = 8.44). The descriptive data for this group is

summarized in Table 4.1.

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Table 4.1

Descriptive Data for All Groups

Category

QEEG

Group

(n = 21)

IVA

Group

(n = 10)

DSMD

Group

(n = 14)

BRIEF

Group

(n =12)

Age M (SD)

Children

Adults

21.19 (18.12)

15

6

26.80 (19.84)

5

5

10.86 (2.91)

14

0

20.25 (19.97)

10

2

Gender

Male

Female

10

11

5

5

7

7

6

6

Ethnicity

White

Asian

Latino

17

2

2

9

0

1

10

2

2

11

1

0

Socioeconomic Status

Low

Medium

High

5

14

2

3

5

2

3

9

2

2

9

1

Diagnosis or Condition

ADHD-Inattentive

ADHD-Combined

ADHD-C/Anxiety

ADHD-C/ASD

ADHD-C/LD

Anxiety/Depression

ASD

Bipolar

Executive Function

OCD/Exec Function

RAD

4

7

1

1

1

1

1

1

2

1

1

3

4

1

0

1

0

0

0

1

0

0

2

5

0

1

1

1

1

1

1

0

1

2

2

0

1

1

1

1

0

2

1

1

Medication

No

Yes

Yes to off

Yes to reduced

16

2

2

1

8

1

1

0

11

1

1

1

10

1

1

0

# Sessions pre-to-post

M (SD)

# Weeks for treatment

M (SD)

# Weeks pre to post

assessment M (SD)

10.90 (3.88)

11.76 (5.19)

15.10 (10.03)

9.70 (3.92)

9.40 (4.40)

13.20 (11.11)

11.43 (4.13)

12.57 (5.60)

15.36 (8.63)

11.83 (2.69)

13.50 (3.97)

16.17 (8.44) Note. ADHD: Attention Deficit Hyperactivity Disorder; ASD: Autism Spectrum Disorder; LD: Learning

Disorder; RAD: Reactive Attachment Disorder; OCD: Obsessive-Compulsive Disorder.

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Data set limitations. Given the data for this study was derived from a

retrospective collection of information from existing files, there are some inherent

limitations. Yet, while these limitations are unavoidable, taking data from real-world

records gives an opportunity to evaluate an intervention using realistic information

typically found in a clinical setting. Such was the case with this study. One example is

with regard to medication usage of the subjects. In a true experimental setting, having no

medication usage in all subjects would be ideal; however, NF clinicians routinely have

clients who seek out NF while still taking medications. The frequency of cases involving

medication use in this study, with an overall five out of 21 for the QEEG group, two out

of 10 in the IVA group, three out of 14 for the DSMD group, and two out of 12 for the

BRIEF group, was a fairly accurate representation of the overall population that has been

seen in this practice for close to 15 years. Therefore, while an argument could be made

that a data set with no medication usage may provide for more credible results; in reality,

the data in this study made the results more generalizable to the population of those who

actually seek NF services.

The other example in this study, impacted by a fixed data set, was regarding the

number of weeks from pre assessment to post assessment. The apparent great variability

in number of weeks was accounted for by two outlier cases on the high end (i.e. 37 and

43 weeks), and one outlier case on the low end (i.e. 2 weeks). If these outliers were

excluded, the range of number of weeks would have been from six to 26 for the QEEG

and DSMD groups, from seven to 15 for the IVA group, and from seven to 26 for the

BRIEF group. This then, better explains why the group means averages of the number of

weeks from pre assessment to post assessment (as seen in table 4.1) are 15 weeks for the

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QEEG and DSMD groups, 13 weeks for the IVA group, and 16 weeks for the BRIEF

group. Also of note, it is important to realize that the number of weeks from pre to post

assessment does not represent the number of sessions; it is only the time elapsed between

assessment points. To clarify this aspect, the number of sessions pre-to-post treatment, as

well as the number of weeks for the treatment, are reported to better illustrate the

timeframes and sessions performed during the 19ZNF.

Data Analysis Procedures

The data analysis procedures were conducted with no deviation from what was

described in the previous Methodology chapter. Given this study consisted solely of data

collection and analysis, the greatest source of error was data collection and data entry

errors, which would negatively impact this research with inaccurate results. This was

mitigated by being careful in the data handling, as well as double checking the data

collection and entry. An additional check of data processing was accomplished by

separately repeating the data collection and analysis steps two separate times, thus

providing a thorough accuracy check of the values collected and analyzed.

Prior to analysis, using SPSS v. 21, the data were reviewed and there were no

outliers or missing data found. For each data group (IVA, DSMD, BRIEF, and QEEG) an

SPSS file was set up and variables such as Case Number, Pre and Post variables for each

scale were established. Next, data was transferred from the data collection spreadsheet to

the appropriate SPSS columns. Then, Difference variables were created, for each scale,

using the SPSS command sequence of Transform>Compute Variable>Create Difference

to calculate the difference score between the pre and post scale values.

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Normality checks. Prior to running parametric t tests, for repeated measures data,

checking to ensure the difference scores meet the necessary assumption of normality is an

important step to demonstrate the validity and reliability of the data analysis and

inference (Gravetter & Wallnau, 2010). There are various techniques for checking

normality. Graphical methods include histograms or Q-Q plots, while numerical methods

include skewness/kurtosis coefficients or formal normality tests. As shown in Appendix

C, the Q-Q plots for all the difference scores analyzed provide visual evidence of the

difference scores meeting the assumption of normality. However, only formal normality

tests provide conclusive evidence, with specific cut-off values (i.e. p values), that the

requirement for a normal distribution has been met (Razali & Wah, 2011). In a study

comparing four formal normality tests (i.e. Shapiro-Wilk, Kolmogorov-Smirnov,

Lilliefors, and Anderson-Darling), Razali and Wah (2011) found the Shapiro-Wilk test to

be the most powerful for all sample sizes and distribution types. Therefore, the Shapiro-

Wilk test was also used to check the difference scores for normality. This was

accomplished using the SPSS command sequence of Analyze>Descriptive

Statistics>Explore. The Shapiro-Wilk computations for all scales, in all groups, resulted

in p > .05 (ranging from p = .084 to p = .980); thus ensuring the difference scores met the

normality assumption. Meeting this assumption provides confidence that the statistical

analysis yields reliable and valid results (Razali & Wah, 2011). Therefore, the Shapiro-

Wilk testing indicates the validity and reliability of the interpretation of the data as well

as the inference of the data in this study was demonstrated. A breakdown of the

difference scores Shapiro-Wilk p values are provided in Table 4.2.

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Table4.2

Shapiro-Wilk Results for Difference Scores

Groups

Scales

Shapiro-Wilk

p Values

IVA (n = 10)

Audio Attention

Visual Attention

Full Scale Attention

DSMD (n = 14)

Externalizing

Internalizing

Total

BRIEF (n = 12)

BRI

MI

GEC

QEEG (n = 21)

Absolute Power

Relative Power

Coherence

.429

.314

.980

.771

.336

.582

.178

.934

.084

.930

.778

.437

Paired t tests. To compute the paired t tests, for the three scales in each group,

the SPSS command sequence executed was Analyze>Compare Means>Paired Samples T

Test. For each scale, the Pre variable was moved to the Variable 1 position and the Post

variable was moved to the Variable 2 position. Given the directional nature of all

hypotheses, it was necessary to divide the SPSS-computed 2-tailed p value by two in

order to derive the 1-tailed p value. Finally, the Hedges’ d effect sizes were calculated

using the MetaCalc module of the Metawin 2.1 software.

The analysis for each of the psychometric assessment groups maintained

alignment with the associated research questions by including the specified scales; those

which most closely associate with the constructs of interest. These included the Attention

scales for the IVA group, the Internalizing, Externalizing, and Total scales for the DSMD

group, and the composite indices of Behavior Regulation, Metacognition, and Global

Executive for the BRIEF group. For the QEEG group, analyzing whether the post z-

96

scores are closer to the mean maintains alignment with the z-score research question.

Moreover, the paired t test analyses, where the means of the pre values are compared to

the means of the post values, was appropriate for the one-group pretest-posttest design of

this study.

Results

For all of the research questions in this study, the group means direction of

change was first determined; then, the paired t test was performed to compare the means

of the pre and post scores. Finally, the Hedges’ d effect size (Hd) was calculated. No

outliers were found in the group means data analyzed. Line graphs showing the pretest

and posttest scores, for each individual subject, are shown in Appendix D to provide a

detailed picture of individual assessments.

Research question 1a: IVA group. Does 19ZNF improve attention as measured

by the IVA assessment?

Ha1a: The post scores will be higher than the pre scores for the IVA

assessment.

H01a: The post scores will be lower than, or not significantly different

from, the pre scores of the IVA assessment.

For this research question, the scales of Auditory Attention, Visual Attention, and

Full Scale were evaluated; with the threshold for clinical significance being ≤ 85. The

mean post scores were higher than the pre scores for all scales; thus the change was in the

predicted direction. The mean of the Auditory Attention scale pre scores was 86.50 (SD =

14.11), 95% CI [76.40, 96.60], and the mean of the post scores was 106.20 (SD = 10.76),

[98.50, 113.90]. The mean of the Visual Attention scale pre scores was 83.60 (SD =

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19.37), [69.74, 97.46], and the mean of the post scores was 103.70 (SD = 13.21), [94.25,

113.15]. The mean of the Full Scale pre scores was 83.40 (SD = 18.23), [70.36, 96.44],

and the mean of the post scores was 105.60 (SD = 12.25), [96.84, 114.36]. Moreover, the

mean pre scores for all three scales were at or below the cutoff threshold indicating

clinical significance; and the mean post scores for all three scales were above the clinical

cutoff threshold. The one-tailed t test results showed the pre and post scores differed

significantly; with the Auditory Attention scale t(9) = -4.29, p = .001, Hd = 1.84; the

Visual Attention scale t(9) = -3.00, p = .008, Hd = 1.29; and the Full Scale t(9) = -3.78, p

= .002, Hd = 1.62. Therefore, the null hypothesis was rejected in favor of the alternative

hypothesis, as the post scores were higher than the pre scores for the IVA assessment;

thus suggesting improvement in attention. See Figure 4.1 for a graphical representation of

the pre and post scale scores.

Figure 4.1. Mean IVA group standard scores before and after 19ZNF

sessions. The dotted line indicates threshold for clinical significance;

values at or below the line suggest clinically relevant symptoms. Post

values above the line suggest improvements in attention. All post scores

are statistically significant at p ≤ .008.

98

Research question 1b: DSMD group. Does 19ZNF improve behavior as

measured by the DSMD assessment?

Ha1b: The post scores will be lower than the pre scores for the DSMD

assessment.

H01b: The post scores will be higher than, or not significantly different

from, the pre scores of the DSMD assessment.

For this research question, the scales of Externalizing, Internalizing, and Total

were evaluated; with the threshold for clinical significance being ≥ 60. The mean post

scores were lower than the pre scores for all scales; thus the change was in the predicted

direction. The mean of the Externalizing scale pre scores was 68.21 (SD = 15.49), 95%

CI [59.27, 77.16], and the mean of the post scores was 57.71 (SD = 12.78), [50.28,

65.14]. The mean of the Internalizing scale pre scores was 66.21 (SD = 9.82), [60.55,

71.88], and the mean of the post scores was 57.29 (SD = 9.85), [51.60, 62.97]. The mean

of the Total scale pre scores was 65.00 (SD = 10.58), [58.89, 71.11], and the mean of the

post scores was 55.64 (SD = 10.76), [49.43, 61.86]. Moreover, the mean pre scores for all

three scales were above the cutoff threshold indicating clinical significance, and the mean

post scores for all three scales were below the clinical cutoff threshold. The one-tailed t

test results showed the pre and post scores differed significantly; with the Externalizing

scale t(13) = 4.97, p = .000, Hd = 1.83; the Internalizing scale t(13) = 6.43, p = .000, Hd

= 2.36; and the Total scale t(13) = 9.36, p = .000, Hd = 3.42. Therefore, the null

hypothesis was rejected in favor of the alternative hypothesis, as the post scores were

99

lower than the pre scores for the DSMD assessment; thus suggesting improvement in

behavior. See Figure 4.2 for a graphical representation of the pre and post scale scores.

Figure 4.2. Mean DSMD group standard scores before and after 19ZNF

sessions. The dotted line indicates threshold for clinical significance;

values at or above the line suggest clinically relevant symptoms. Post

values below the line suggest improvements in behavior. All post scores

are statistically significant at p = .000.

Research question 1c: BRIEF group. Does 19ZNF improve executive function

as measured by the BRIEF assessment?

Ha1c: The post scores will be lower than the pre scores for the BRIEF

assessment.

H01c: The post scores will be higher than, or not significantly different

from, the pre scores of the BRIEF assessment.

100

For this research question, the scales of BRI, MI, and GEC were evaluated;

with the threshold for clinical significance being ≥ 65. The mean post scores were lower

than the pre scores for all scales; thus the change was in the predicted direction. The

mean of the BRI scale pre scores was 71.00 (SD = 11.40), 95% CI [63.77, 78.23], and the

mean of the post scores was 60.17 (SD = 10.27), [53.64, 66.69]. The mean of the MI

scale pre scores was 76.08 (SD = 8.24), [70.85, 81.32], and the mean of the post scores

was 65.67 (SD = 10.36), [59.08, 72.25]. The mean of the GEC scale pre scores was 75.75

(SD = 9.33), [69.82, 81.68], and the mean of the post scores was 64.50 (SD = 9.91),

[58.20, 70.80]. Moreover, the mean pre scores for all three scales were above the cutoff

threshold indicating clinical significance, and the mean post scores for all three scales

were below the clinical cutoff threshold. The one-tailed t test results showed the pre and

post scores differed significantly; with the BRI scale t(11) = 4.37, p = .001, Hd = 1.72;

the MI scale t(11) = 4.39, p = .001, Hd = 1.73; and the GEC scale t(11) = 4.66, p = .000,

Hd = 1.84. Therefore, the null hypothesis was rejected in favor of the alternative

hypothesis, as the post scores were lower than the pre scores for the BRIEF assessment;

thus suggesting improvement in executive function. See Figure 4.3 for a graphical

representation of the pre and post scale scores.

101

Figure 4.3. Mean BRIEF group standard scores before and after 19ZNF

sessions. The dotted line indicates threshold for clinical significance;

values at or above the line suggest clinically relevant symptoms. Post

values below the line suggest improvements in executive function. All

post scores are statistically significant at p ≤ .001.

Research question 2: QEEG group. Does 19ZNF improve electrocortical

function as measured by QEEG z-scores such that the post z-scores are closer to the mean

than pre z-scores?

Ha2: The post z-scores will be closer to the mean than the pre z-scores.

H02: The post z-scores will be farther from the mean, or not significantly

different from, the pre z-scores.

For this research question, the QEEG metrics of Absolute power, Relative power,

and Coherence were evaluated; with the targeted transformed z-score threshold value

being z ≥ 1.00. The mean post z-scores were lower than the pre z-scores for all metrics;

thus the change was in the predicted direction and the z-scores were closer to the mean.

The mean of the Absolute power pre z-scores was 1.46 (SD = 0.28), 95% CI [1.33, 1.59],

102

and the mean of the post scores was 1.03 (SD = 0.37), [0.87, 1.20]. The mean of the

Relative power pre z-scores was 1.51 (SD = 0.22), [1.41, 1.61], and the mean of the post

scores was 1.13 (SD = 0.35), [0.97, 1.29]. The mean of the Coherence pre z-scores was

1.46 (SD = 0.14), [1.40, 1.53], and the mean of the post scores was 0.96 (SD = 0.32),

[0.82, 1.11]. Moreover, the mean pre scores for all metrics were above 1.00, and the

mean post scores for all metrics approached or were below 1.00. The one-tailed t test

results showed the pre and post scores differed significantly; with the Absolute power

t(20) = 7.73, p = .000, Hd = 2.29; the Relative power t(20) = 5.22, p = .000, Hd = 1.76;

and the Coherence t(20) = 6.55, p = .000, Hd = 1.88. Therefore, the null hypothesis was

rejected in favor of the alternative hypothesis, as the post z-scores were closer to the

mean than the pre z-scores; thus suggesting improvement in electrocortical functioning.

See Figure 4.4 for a graphical representation of the pre and post scale scores.

Figure 4.4. Mean QEEG group targeted z-scores before and after 19ZNF

sessions. The dotted line indicates threshold for inclusion as targeted

z-scores; values above the line suggest electrocortical dysfunction. Post

values at or below the line suggest improvements in electrocortical

function. All post scores are statistically significant at p = .000.

103

Summary

The research questions for this study asked if the independent variable of 19ZNF

improved attention, behavior, executive function, and electrocortical function. The

dependent variables to test the hypotheses included the scaled scores from the IVA,

DSMD, and BRIEF clinical assessments and QEEG z-scores. The difference scores were

normally distributed, thus supporting the use of one-tailed t tests to compare the pre to the

post scores for each of the dependent variables.

For all pre-post comparisons, the direction of change in the scores was in the

predicted direction for all hypotheses. Moreover, for all the outcome measures, the

averaged scores were beyond the clinically significant threshold before 19ZNF and

changed to no longer being so after 19ZNF. Finally, for all research questions, the null

hypothesis was rejected, in favor of the conclusion that 19ZNF improved attention,

behavior, executive function, and electrocortical function (respective to each hypothesis).

All differences were statistically significant, with results ranging from p = .000 to p =

.008; and Hd values ranging from 1.29 to 3.42. Table 4.3 provides a cumulative summary

of the results of these findings for all groups.

In the chapter that follows, a discussion of these findings will be presented.

Conclusions and interpretations regarding the contributions of this research will be

offered. Furthermore, a review of the implications (practical, theoretical, and future) of

this research, and recommendations for future research and practice will be provided.

104

Table 4.3

Summary of Results – All Groups

Groups

Scales

PRE Scores

M (SD)

POST Scores

M (SD)

t(df)

p

Hedges’ d

IVA

Audio Attention

Visual Attention

Full Scale Attention

86.50 (14.11)

83.60 (19.37)

83.40 (18.23)

106.20 (10.76)

103.70 (13.21)

105.60 (12.25)

-4.29 (9)

-3.00 (9)

-3.78 (9)

.001

.008

.002

1.84

1.29

1.62

DSMD

Externalizing

Internalizing

Total

68.21 (15.49)

66.21 (9.82)

65.00 (10.58)

57.71 (12.87)

57.29 (9.85)

55.64 (10.76)

4.97 (13)

6.43 (13)

9.36 (13)

.000

.000

.000

1.83

2.36

3.42

BRIEF

BRI

MI

GEC

71.00 (11.40)

76.08 (8.24)

75.75 (9.33)

60.17 (10.27)

65.67 (10.36)

64.50 (9.91)

4.37 (11)

4.39 (11)

4.66 (11)

.001

.001

.000

1.72

1.73

1.84

QEEG Z-Scores

Absolute Power

Relative Power

Coherence

1.46 (0.28)

1.51 (0.22)

1.46 (0.14)

1.03 (0.37)

1.13 (0.35)

0.96 (0.32)

7.73 (20)

5.22 (20)

6.55 (20)

.000

.000

.000

2.29

1.76

1.88

105

Chapter 5: Summary, Conclusions, and Recommendations

Introduction

The primary problem this research sought to address was how it was not known,

by way of statistical evaluation of either clinical assessments or QEEG z-scores, if

19ZNF was an effective NF technique. This problem, manifest as a lack of literature,

leaves clinicians and prospective NF clients alike without research-based evidence to

evaluate 19ZNF. Currently, mostly qualitative case-study reports have been found in the

literature. Thus, this study has importance in its aim to fill this empirical gap.

As has been discussed, NF is gaining recognition as an evidence-based

intervention grounded in learning theory. Among the different models developed over the

last 40 years, 19ZNF is one of the newest. Yet, while 19ZNF is reported to lead to

improved clinical outcomes in fewer sessions than traditional NF, and a growing number

of clinicians are adding this model to their practice, the peer-review literature is lacking

regarding the efficacy of the model. This study was different in its use of group means

data to directly compare pre and post outcome measure variables, to include QEEG data,

to begin an evaluation of efficacy of 19ZNF. The use of a quasi-experimental design in

this research, which has not been typical in prior 19ZNF evaluations, provides baseline

research for investigating the efficacy of 19ZNF. The use of quantitative methods, with

group means data, contributes to the base of knowledge regarding 19ZNF by providing

statistical analysis, which allows for greater generalization over qualitative and/or case

study investigations.

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Summary of the Study

This chapter aims to first present a summary and conclusions of the study. Next,

practical, theoretical, and future implications will be reviewed. Finally, future research

and practice recommendations will be discussed.

This retrospective pretest-posttest study investigated if 19ZNF improved

attention, behavior, executive function, and electrocortical functioning. To that end, the

research questions asked if 19ZNF improved: Attention as measured by the IVA,

behavior as measured by the DSMD, executive function as measured by the BRIEF, and

electrocortical function as measured by QEEG z-scores. Paired t tests were performed to

compare the means of four outcome measures; which included three clinical assessments

(IVA, DSMD, and BRIEF) and QEEG z-scores. Each of the clinical assessments framed

a sample group such that the efficacy of 19ZNF was evaluated, as it relates to the

particular neuropsychological constructs of attention (n = 10), behavior (n = 14),

executive function (n = 12), and additionally as related to electrocortical functioning (n =

21). The focus of the IVA sample group was attention, and the scales specific to attention

were the Auditory Attention, Visual Attention, and Full Scale. The focus of the DSMD

sample group was behavior, and the scales specific to behavior were the Externalizing,

Internalizing and Total. The focus of the BRIEF sample group was executive function,

and the composite scales included were BRI, MI, and GEC. The focus of the QEEG

sample group was electrocortical function, and the QEEG metrics included were

Absolute power, Relative power, and Coherence.

Overall, the makeup of the sample was a diagnostically diverse mixture of adults

and children, with most diagnoses related to ADHD. The sample consisted of more

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children (QEEG = 15, IVA = 5, DSMD = 14, BRIEF = 10) than adults (QEEG = 6, IVA

= 5, DSMD = 0, BRIEF = 2). Other sample characteristics consistent across all groups

are they were evenly divided with respect to gender, were primarily ethnically white, and

were mostly medium SES.

In Chapter 1, an orienting framework of the study was presented to include the

problem statement and study purpose, as well as the methodology rationale and nature of

the research design. In Chapter 2, a review of the literature was presented. The history

and background of ZNF was first addressed; then, the theoretical foundations and

conceptual frameworks of NF and QEEG were presented. Theoretical frameworks

supporting the models of traditional NF, QNF, and ZNF were then reviewed, as were key

NF themes related to applications of QNF and the emergence of 19ZNF. Moreover,

outcome measures suitable for ZNF research were discussed. The focus of Chapter 3was

the methodology of the study and Chapter 4 presented research findings and results.

Summary of Findings and Conclusion

Operant conditioning is the theoretical foundation of NF, with demonstrated

efficacy in improving brain functioning and clinical symptoms, through the resulting

electrocortical changes. However, whether this also holds true for the new 19ZNF model

has been an outstanding question. As discussed in Chapter 1, and again in Chapter 3, the

aim of this study was to provide the beginnings of an evidence-based foundation for the

efficacy of 19ZNF. The focus was to evaluate if 19ZNF would result in improved clinical

symptoms and electrocortical function as measured by the identified outcome measures.

In general, the findings of this study were that attention, behavior, executive function,

and electrocortical function all improved after approximately ten 19ZNF sessions; with

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the number of sessions ranging from an average of 9.70 to 11.83 sessions across the four

groups. This study also supported the clinical reports of Thatcher (2013) and Wigton

(2013) that 19ZNF results in improvement in clinical symptoms in fewer sessions than

the 40+ sessions typical in traditional NF. Also notable, is that the frequency of the

sessions was an average of once per week, rather than the two to three times per week as

is typical of traditional NF or QNF. Each finding will next be reviewed separately, to

further discuss the significance of this study as related to the identified constructs of

attention, behavior, executive function, and electrocortical function.

Research question 1a: IVA group. Does 19ZNF improve attention as measured

by the IVA assessment? In answering this research question, as seen in Table 4.3, the

post scores were higher than the pre scores for the IVA, thus lending support for attention

being improved. Although this group was made up of subjects with varying diagnoses

(though mostly associated with ADHD), as a collective group, they all initially exhibited

symptoms of attention dysfunction; as all the group means Attention scales scores fell at

or below the clinically significant range (Auditory Attention = 86.50, Visual Attention =

83.60, and Full Scale = 83.40). As was expected, 19ZNF resulted in a positive clinical

outcome of improved attention, as the subjects’ performance on the posttest assessment

significantly improved. After 19ZNF, all the included group means Attention scales were

no longer in the clinically significant range (Auditory Attention = 106.2, Visual Attention

= 103.70, and Full Scale = 105.60). The effect sizes for the three scales (1.84, 1.29, and

1.62, respectively) are all considered very large. Therefore, the results of this research

question were both clinically and statistically significant.

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Given that no prior 19ZNF studies were found which analyzed IVA data as an

outcome measure, no direct comparison to prior research is possible. Moreover, there

were no QNF studies found incorporating the IVA as an outcome measure. In looking at

traditional NF studies, while Knezevic, et al. (2010) incorporated the IVA in their study,

they did not use the any of the composite Attention scales. The Fritson et al. (2008) study

is not a relevant comparison as they used a sample of non-clinical college students.

Finally, in the research of Steiner, et al. (2011), the only comparable scale used was the

Attention Full scale; yet, with an n = 6, while the post scores were in the desired

direction, the pre-post difference scores were not statistically significant.

Research question 1b: DSMD group. Does 19ZNF improve behavior as

measured by the DSMD assessment? In answering this research question, as seen in

Table 4.3, the post scores were lower than the pre scores for the DSMD, thus lending

support for behavior being improved. Although this group was made up of subjects with

varying diagnoses, as a collective group, they all initially exhibited symptoms of

behavioral issues; as all the included group means scales scores fell at or above the

clinically significant range (Externalizing = 68.21, Internalizing = 66.21, and Total =

65.00). As was expected, 19ZNF resulted in a positive clinical outcome of improved

behavior, as the subjects’ scores on the posttest assessment significantly improved. After

19ZNF, all the included group means scales were no longer in the clinically significant

range (Externalizing = 57.71, Internalizing = 57.29, and Total = 55.64). The effect sizes

for the three scales (1.83, 2.36, and 3.42, respectively) are all interpreted as being very

large; and are the largest effect sizes in this study. Therefore, the results of this research

question were both clinically and statistically significant.

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To date, no prior NF studies (ZNF, QNF, or traditional NF) have conducted

outcome measure analysis with the DSMD; as such, there are no relevant existing studies

with which to directly contrast or compare. However, the DSMD scales of Externalizing,

Internalizing, and Total correlate well to the similarly named scales of the CBCL. Huang-

Storms et al. (2006) used the CBCL as an outcome measure in their retrospective pretest

posttest study evaluating traditional NF. All post scores were in the desired direction and

difference scores for all scales were statistically significant (p < .01) with medium to

large effect sizes (Externalizing, Cohen’s d = .94; Internalizing, Cohen’s d = .59; Total,

Cohen’s d = .78).

Research question 1c: BRIEF group. Does 19ZNF improve executive function

as measured by the BRIEF assessment? In answering this research question, as seen in

Table 4.3, the post scores were lower than the pre scores for the BRIEF, thus lending

support for executive function being improved. Although this group was made up of

subjects with varying diagnoses, as a collective group, they all initially exhibited

symptoms of compromised executive function; with all the included group means scales

scores falling at or above the clinically significant range (BRI = 71.00, MI = 76.08, and

GEC = 75.75). As was expected, 19ZNF resulted in a positive clinical outcome of

improved executive function, as the subjects’ scores on the posttest assessment

significantly improved. After 19ZNF, all the included group means scales were no longer

in the clinically significant range (BRI = 60.17, MI = 65.67, and GEC = 64.50). The

effect sizes for the three scales (1.72, 1.73, and 1.84, respectively) are all interpreted as

being very large. Therefore, the results of this research question were both clinically and

statistically significant.

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Here too, no prior 19ZNF studies were found which conducted outcome measure

analysis with the BRIEF; thus, no direct comparison to prior research is possible.

However, the Orgim and Kestad (2013) study, which compared QNF to medication for

ADHD, included the BRI and MI scales of the BRIEF among various outcome measures.

For the BRIEF scales analyzed, while the post scores were in the desired direction for

both groups, the difference between NF and medication groups were not significant. The

Drechsler et al. (2007) study compared SCP NF to group therapy and incorporated the

BRI and MI scales of the BRIEF as two of their outcome measures. Their findings

indicated a statistically significant (p = .004) improvement for NF, more than group

therapy, on the MI scale of the BRIEF; whereas there were no significant differences for

NF versus group therapy for the BRI scale. Finally, Steiner et al. (2011) incorporated the

GEC scale of the BRIEF as one of many outcome measures in comparing traditional NF

to computerize attention training to a waitlist control. For all groups, for the primary

parent and co-parent ratings, all post scores moved in the desired direction, however, only

the computerized attention training resulted in a significant difference (p < .05) for the

GEC scale.

Research question 2: QEEG group. Does 19ZNF improve electrocortical

function as measured by QEEG z-scores, such that the post z-scores are closer to the

mean than pre z-scores? In answering this research question, as seen in Table 4.3, the

post z-scores were closer to the mean than the pre z-scores, thus lending support for

electrocortical function being improved. Although this group was made up of subjects

with varying diagnoses, as a collective group, they all exhibited electrocortical

dysregulation; with all the targeted z-scores group means falling above the z-score

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threshold (Absolute power = 1.46, Relative power = 1.51, and Coherence = 1.46). As was

expected, 19ZNF resulted in a positive clinical outcome of improved electrocortical

function, as the subjects’ averaged targeted z-scores on the posttest assessment

significantly improved. After 19ZNF, the targeted z-score group means for Absolute

power (1.03) and Coherence (0.96) were at or below the z-score threshold, with the

Relative power (1.13) approaching the threshold. The effect sizes for the three scales

(Absolute power = 2.29, Relative power = 1.76, and Coherence = 1.88) are all interpreted

as being very large. Therefore, the results of this research question were both clinically

and statistically significant. Moreover, these findings suggested that, as a group, the

subjects’ QEEG z-scores normalized as a result of 19ZNF; and perhaps more

importantly, the normalization was accompanied by clinical symptom improvement.

As has been stated, few NF studies make use of QEEG metrics as outcome

measures. More so, as of this writing, no prior NF studies (ZNF, QNF, or traditional NF)

have been found incorporating a measure of overall QEEG normalization. Thus, there are

no relevant existing studies with which to contrast or compare.

Conclusions. The literature reviewed for this study found both traditional NF and

QNF studies consistently employed retrospective pretest-posttest designs. This research

was consistent with those prior works. Significant differences were found between the

pre and post scores, thus indicating positive clinical outcomes. However, this research

was also innovative in that it made use of QEEG metrics, as outcome measures, to

provide an overall measure of the distance from the mean, for determining overall

normalization of the z-scores. Here too, the pre to post score differences were significant

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for all metrics indicating normalization of the QEEG z-scores, thus indicating improved

electrocortical function.

Arns et al. (2009, 2012) have discussed effect sizes in studies evaluating NF to

treat ADHD. For traditional NF models, Hd effect sizes were 0.7 and 1.0 for hyperactive

and attention symptoms, respectively; yet for the QNF models, Hd effect sizes were 1.2

and 1.8 (hyperactive and attention symptoms, respectively). In this research, Hd effect

sizes ranged from 1.29 to 3.42, with an average of 1.97. Therefore, the effect sizes for

this study were similar, or greater, than what has been reported for QNF and traditional

NF models. Moreover, if NF efficacy is defined in terms of large effect sizes when

comparing pre-post outcome measure data (Arns et al., 2012), then the effect sizes of this

study support 19ZNF as being effective.

Therefore, as was proposed in Chapter 1, it is reasonable to conclude that the

theory of operant conditioning, upon which NF is founded, can be expanded to include

19ZNF. It is also reasonable to conclude that, in the context of this study, the findings

supported the efficacy of 19ZNF in improving attention, behavior, executive function,

and electrocortical function. Thus, this research addressed the literature gap and begins to

lend credence to the position that 19ZNF could be considered an evidence-based

intervention. Further, this study demonstrated that QEEG z-scores data can be used for

group comparison studies, in a way not previously developed; thus, this study has the

potential for cultivating future QEEG-based research.

Implications

The objective of this research was a comparison of outcome measures before and

after 19ZNF to evaluate the efficacy of this NF intervention. In reviewing the theoretical

114

framework discussed in the literature review, certain elements are pertinent to the

findings of this research. Hughes and John (1999) demonstrated EEG/QEEG measures to

be sensitive to psychiatric disorders. The QNF model (which informs the 19ZNF model)

is founded on the premise that electrocortical dysfunctions correspond with clinical

symptoms and mental disorders (Coben & Myers, 2010; Collura, 2010; Walker, 2010a),

such that clinical symptoms can be linked to brain dysregulation (Thatcher, 2013).

Further, when NF results in symptom resolution, together with QEEG normalizing, this

represents an improvement in electrocortical functioning (Arns et al., 2012; Walker,

2010a). Therefore, the findings of this study (with the 19ZNF protocol of QEEG

normalization) were consistent with the multiple reports in the literature suggesting

QEEG normalization protocols bring about clinical benefits (Arns et al., 2012; Breteler et

al., 2010; Collura, 2008; Orgim & Kestad, 2013; Surmeli et al., 2013; Surmeli & Ertem,

2009, 2010; Walker, 2009. 2010a, 2011, 2012a).

Theoretical implications. QEEG normalization is a theoretical construct which

has grown in popularity with the advent of the QNF model; as has the use of individually

tailored QEEG-based protocols to bring about that normalization. Additionally, clinical

reports have suggested 19ZNF may exhibit better performance than traditional NF. These

findings supported 19ZNF as a NF modality which can bring about both QEEG

normalization and symptom improvement. More so, it can do so quite efficiently, as

evidenced by the results of this study occurring on average of within 10 sessions, at a

target frequency of once per week.

As discussed in the literature review, the greater specificity that QEEG-based

methods allowed in treatment also creates methodological challenges due to the need to

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account for both positive and negative z-scores. This study’s method of transforming the

z-scores to the absolute value, then tracking pre to post changes of the targeted z-scores,

presented an innovative methodology for measuring overall normalization of the QEEG.

If further validated, this approach has the potential to open new avenues for QEEG-based

research, both within the NF community as well as the broader neuroscience fields.

The implications of this study, as related to cognition and instruction, are twofold.

First, the findings suggested 19ZNF improves the attention and executive function

components of cognition. Second, when cognition improves, more mental resources are

made available for an individual to better engage in instructional processes. The findings

of this study also suggested that 19ZNF can improve behavior. In group educational

settings, when disruptive behavior improves, distractions to other learners are reduced

and the effectiveness of the instructional environment can be enhanced. Therefore, this

study lent support to 19ZNF as benefiting both cognition and instruction.

Practical implications. This research begins to address the literature gap

regarding evidence-based findings of 19ZNF. Thus, this study can provide NF clients and

clinicians with information regarding its efficacy in improving attention, behavior,

executive function, and electrocortical function. Furthermore, it suggests that 19ZNF may

address the need for 40+ sessions for success with NF. If 19ZNF is shown to be an

evidence-based intervention which requires fewer sessions than tradition NF or QNF,

clients will benefit through the associated cost savings. Also of note, while not a specific

focus in this research, is that the 19ZNF in this study occurred at a frequency of only

once per week, rather than the two to three times per week as other models. These

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aspects, taken together, may potentially serve to reduce resistance of third-party payers to

include NF as covered services.

Future implications. Future implications of this study depend on future research.

This study only provided the beginning steps of forming an evidence-based framework

for 19ZNF. As will be discussed below, much remains to be investigated and evaluated

through further research. However, that being said, this study has the potential of

widening the acceptance of 19ZNF, as well as opening new frontiers for QEEG-based

research.

Strengths of this study include being a first quantitative analysis of group means

data from 19ZNF, of which, as of this writing, none has been found. Thus, this research

contributed in taking the empirical evaluation of 19ZNF beyond clinical reports and case

study presentations. Moreover, data coming from a real-world clinical setting suggests

clinicians employing this new model may have similar results. Given the pretest-posttest

design, and the group means averaged time between pre and post assessments ranged

from 13 to 16 weeks (see table 4.1), the previously identified limitation of potential

maturational or history effects likely had minimal impact on the findings. This, then,

increased the credibility of the conclusions. However, remaining weaknesses, inherent in

retrospective studies in clinical settings, included limitations already discussed, such as

small sample size, lack of a separate control groups (lack of randomization), or

comparison to traditional and QNF models. Therefore, recommendations for further

research are next provided.

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Recommendations

As discussed in the Limitations section of Chapter 3, the question of efficacy

cannot be fully explored without further research. More so in investigating 19ZNF as

being superior to other NF approaches. Therefore, specific recommendations for further

research are presented. Additionally, recommendations for practice will also be reviewed.

Recommendations for future research. As has been discussed, this study was

only a beginning step toward proving 19ZNF as efficacious; thus the recommendations

herein serve to propose next steps in forwarding this line of research. A notable

significance of this study, in advancing scientific knowledge, was that it filled the gap of

a lack of quantitative studies evaluating 19ZNF. However, the gap is large and more

research is needed. Therefore, all the following recommendations would be best

implemented through the use of quantitative methodologies, in order to apply evidence-

based strategies and statistical analysis to evaluate outcomes of 19ZNF as a treatment

intervention.

A single study is insufficient to fully validate the efficacy of any treatment

intervention. Thus, replication of this study would add to the scientific integrity of the

results; however, doing so with larger sample sizes would, of course, be recommended.

Next, follow-up studies are a needed area of focus. While 19ZNF may be effective in the

short-term, the question of whether the benefits hold over time is still outstanding. With

19ZNF being new among other approaches, ones backed by more research, direct

comparisons to the traditional or QNF models are needed; particularly with randomized

assignments. Additional suggestions for randomized control group research are for

comparisons to waitlist groups. However, randomized controlled methods are less

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feasible in clinical settings; and as such, these studies will likely require university and/or

grant-supported research settings (more conducive to true experimental designs) to

complete. Other comparison research should also explore comparisons of 19ZNF using

surface montages (as with this study) to 19ZNF using inverse-solution montages (e.g.

LORETA).

As has been discussed, few NF studies employ QEEG metrics as a direct outcome

measure; and even fewer do so in analyzing group means data. Therefore, an additional

notable significance of this study, in advancing scientific knowledge, is the novel

development of a measure of overall QEEG normalization, by tracking the pre-post

values of the targeted transformed z-scores. Here too, though, replication and further

validation is needed. Also recommended is an investigation of whether z ± 1.00 is an

optimal threshold value to determine targeted z-scores.

Recommendations for practice. Both NF clinicians and prospective clients will

benefit from reviewing this study. Researchers will also find this study of interest in

furthering what is known about NF, and/or using QEEG metrics as outcome measures in

NF or other QEEG-based investigations. For clinicians employing 19ZNF, who do not

already do so, incorporating the regular use of pre and post outcome measures, and

gathering pre-session baseline QEEG data, is important to furthering what is known

about 19ZNF. Currently, 19ZNF is in its infancy, and likely will face resistance in the

scientific community, much the same as traditional NF has until only recent years. The

settings where conventional experimental work occurs (i.e. grant-funded and/or

university laboratories) may be less likely to embrace research with newer 19ZNF

models, in favor of traditional NF models; at least in the short term. As a result, the

119

clinical setting is currently the primary source of data to evaluate 19ZNF. Therefore,

performing quality pre-post assessments, and then moving forward in research with that

data, will be necessary to advance the acceptance of 19ZNF by the wider scientific

community.

As was discussed in Chapter 1, this study has the potential of opening doors to

future QEEG-based research, in demonstrating that z-scores from QEEG data can be used

for group comparison studies, in a way not previously developed. In moving forward

with this line of research, this study proposed a method for using QEEG metrics for

measuring the degree of normalization. Therefore, incorporating QEEG data as outcome

measures is a practical reality for NF researchers. Thus, practice recommendations are for

including these metrics in future research.

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Appendix A

Test Distribution Limitations

Copies of the commercially available BRIEF and DSMD psychometric test

instruments cannot be provided due to copyright protections. The publisher of the BRIEF,

PAR Incorporated, states on the Permissions page of their website

(www4.parinc.com/ProRes/permissions.aspx) that permission to include copies of an

entire test will not be granted, for any publication, to include dissertations. The publisher

of the DSMD, Pearson Education Incorporated, states in its Terms and Conditions page

of their website (www.pearsonclinical.com/psychology/legal/termsofsale.html) that

reproducing test items/scales is strictly prohibited by law as well as the terms and

conditions for their products. The IVA is a computerized performance test, and as such, is

only accessible by running the program on a computer. Therefore, a printed copy of this

test is not available for inclusion in an appendix.

http://www4.parinc.com/ProRes/permissions.aspx

137

Appendix B

IRB Letter: Determination of Exempt Status

138

Appendix C

Q-Q Plots of Difference Scores

IVA Group

Auditory Scale

Visual Scale

Full Scale

DSMD Group

Externalizing Scale

Internalizing Scale

Total Scale

BRIEF Group

BRI Scale

MI Scale

GEC Scale

QEEG Group

Absolute Power

Relative Power

Coherence

139

Appendix D

Line Graphs of Individual Pre-Post Scores

IVA Group

DSMD Group

BRIEF Group

QEEG Group