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 UMI Number: 3625170 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI 3625170 Published by ProQuest LLC (2014). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 © by Nancy L. Wigton, 2014 All rights reserved. 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 18:3033, 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 decisionmaking 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 zscores? 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 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 zscores 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 2. Instrument(s) 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 zscores 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 zscore 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 subscales (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 zscores 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. 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 metaanalysis 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 decadelong 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 crossculturally), 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, & RamosLoyo, 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 33 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 yearlong 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). 35 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 metacomponent of EEG metrics (i.e. amplitude or connectivity) and ultimately, even when zscores 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 42 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 pretestposttest 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. 45 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 ca...