# Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Participants

#### 2.2. Cognitive and Emotional Checklist

#### 2.3. EEG Collection and QEEG Analysis

#### 2.4. Neurofeedback Intervention (Live Z-Score Training Neurofeedback)

#### 2.5. Statistical Analyses

^{2}) test for categorical variables. To further analyze the changes in QEEG metrics, the absolute Z-score for each participant’s wave was dichotomized (1 if |z-score| ≥ 1.5, and 0 otherwise). Thus, |z-score| ≥ 1.5 was categorized as “out of the normal range.” The primary outcome was a post-intervention change towards a decreased percentage of QEEG waves out of the normal range. The difference in the likelihood of change was analyzed with the odds ratio (OR) and a binary logistic model using a generalized estimating equation (GEE) with an independent correlation structure and robust standard errors. The GEE is a statistical approach that accounts for the correlation between measurements in clustered data (i.e., variables grouped by a cluster identification variable). Unlike ordinary logistic regression, which uses the maximum likelihood estimator, the GEE uses the quasi-likelihood function to estimate the parameters of the studied variables with repeated measures over time. The quasi-likelihood function specifies that the variance of the response variable depends on the mean without assuming a given distribution for the response variable [64]. One of its key features is that it allows the estimation of the correlation structure without having to assume a pre-specified structure [65]. We clustered the QEEG Z-scores by participants. Thus, clusters (i.e., individuals) are independent of one another, but the observations (i.e., waves) are assumed to be correlated within clusters. The GEE model tested the main effects of the group (1 = ni-APF; 0 = li-APF), waves (1 = out of the normal range; 0 = within the normal range), and group-by-wave interaction. The waves within the normal range in the li-APF group were used as the reference category. More details on the GEE model description for our approach are presented in Appendix C.

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**CEC-Learning items [54].

Item | Content | Score |
---|---|---|

11 | Poor Short-Term Memory | 0 1 2 3 |

14 | List Learning Problems | 0 1 2 3 |

29 | Can’t Recall More Than One Request | 0 1 2 3 |

30 | Poor Maths Skills | 0 1 2 3 |

31 | Poor Reading Comprehension | 0 1 2 3 |

42 | Dyslexia | 0 1 2 3 |

43 | Reads Poorly | 0 1 2 3 |

44 | Poor Handwriting | 0 1 2 3 |

48 | Difficulty with Task Sequence | 0 1 2 3 |

49 | Difficulty Learning New Words | 0 1 2 3 |

## Appendix B

**Figure A1.**The patient achieved the percentage of Z-scores within the range (the number 87 in blue represents the percentage of Z values the patient was getting) to get the reinforcer (the number 84 in green represents the percentage of Z values that the patient was asked to put within the range to get the reinforcer).

**Figure A2.**The patient was not achieving the percentage of Z-scores within the range (the number 82 in blue represents Table 84. in green represents the percentage of Z values that the patient was asked to put within the range to get the reinforcer).

## Appendix C

## Appendix D

Parameter | li-APF Group (n = 28) | ni-APF Group (n = 12) | p-Value | ||
---|---|---|---|---|---|

I-APF | Mean | SD | Mean | SD | 0.000 |

8.54 Hz | 0.33 | 10 Hz | 0.31 | ||

CEC-Total | Mean | SD | Mean | SD | p-Value |

Pre | 51 | 6.88 | 49.96 | 8.24 | 0.850 |

Post | 43.75 | 6.85 | 33.50 | 7.23 | 0.000 |

CEC Learning | Mean | SD | Mean | SD | p-Value |

Pre | 18.17 | 1.95 | 18.29 | 3.18 | 0.965 |

Post | 15.08 | 1.93 | 11.46 | 2.66 | 0.000 |

Z-Scores | Ni-APF | Li-APF | p-Value | |
---|---|---|---|---|

Pre/Post | Pre/Post | Pre/Post | ||

F3 | Delta | 0.70 (0.49)/0.62 (0.58) | 0.72 (0.53)/0.62 (0.59) | 0.545/0.825 |

Theta | 0.66 (0.61)/0.58 (0.38) | 0.80 (0.39)/0.92 (0.70) | 0.140/0.121 | |

Alpha | 0.92 (0.63)/0.73 (0.50) | 0.80 (0.49)/0.86 (0.67) | 0.734/0.723 | |

Beta-1 | 1.16 (0.95)/0.67 (0.62) | 0.71 (0.68)/0.98 (0.82) | 0.101/0.626 | |

Beta-2 | 1.16 (0.73)/1.02 (0.55) | 0.77 (0.56)/0.98 (0.77) | 0.152/0.757 | |

Beta-3 | 1.23 (0.81)/0.92 (0.55) | 1.10 (0.71)/1.34 (0.81) | 0.669/0.087 | |

Hi-Beta | 1.52 (0.82)/1.11 (0.73) | 1.90 (1.23)/2.05 (1.19) | 0.479/0.007 | |

F4 | Delta | 0.86 (0.62)/0.54 (0.35) | 0.72 (0.47)/0.61 (0.60) | 0.690/0.768 |

Theta | 0.70 (0.68)/0.51 (0.33) | 0.76 (0.63)/0.68 (0.64) | 0.605/0.848 | |

Alpha | 0.89 (0.68)/0.79 (0.52) | 0.87 (0.73)/0.75 (0.47) | 0.926/0.813 | |

Beta-1 | 1.29 (0.97)/1.04 (0.79) | 0.85 (0.73)/0.84 (0.81) | 0.125/0.215 | |

Beta-2 | 1.16 (0.89)/0.95 (0.69) | 1.00 (0.62)/0.84 (0.77) | 0.757/0.425 | |

Beta-3 | 1.21 (0.79)/0.95 (0.53) | 1.16 (0.64)/1.20 (0.75) | 0.976/0.443 | |

Hi-Beta | 1.49 (0.88)/1.00 (0.86) | 1.49 (1.02)/1.60 (1.42) | 0.906/0.148 | |

P3 | Delta | 0.82 (0.82)/0.70 (0.54) | 0.89 (0.68)/0.71 (0.53) | 0.425/0.976 |

Theta | 0.77 (0.68)/0.54 (0.32) | 0.76 (0.35)/0.67 (0.59) | 0.215/0.768 | |

Alpha | 1.02 (0.63)/0.85 (0.54) | 0.96 (0.70)/0.86 (0.69) | 0.637/0.701 | |

Beta-1 | 1.33 (0.91)/1.03 (0.61) | 0.79 (0.86)/0.86 (0.72) | 0.070/0.262 | |

Beta-2 | 1.50 (0.74)/1.07 (0.55) | 0.96 (0.81)/1.83 (2.36) | 0.063/0.434 | |

Beta-3 | 1.62 (0.78)/1.19 (0.59) | 1.14 (0.80)/1.23 (0.66) | 0.090/1.00 | |

Hi-Beta | 1.94 (1.10)/1.29 (0.61) | 1.99 (0,99)/1.47 (0.89) | 0.779/0.352 | |

P4 | Delta | 0.64 (0.43)/0.61 (0.50) | 0.65 (0.49)/0.79 (0.50) | 0.941/0.294 |

Theta | 0.62 (0.60)/0.59 (0.37) | 0.87 (0.33)/0.74 (0.58) | 0.016/0.516 | |

Alpha | 0.89 (0.57)/0.80 (0.48) | 1.00 (0.53)/1.64 (2.11) | 0.479/0.148 | |

Beta-1 | 1.27 (0.91)/1.11 (0.76) | 0.82 (0.84)/0.80 (0.97) | 0.128/0.152 | |

Beta-2 | 1.47 (0.88)/1.20 (0.73) | 0.98 (0.75)/1.11 (0.80) | 0.092/0.658 | |

Beta-3 | 1.58 (0.83)/1.20 (0.64) | 1.14 (0.74)/1.30 (0.63) | 0.125/0.690 | |

Hi-Beta | 1.82 (1.00)/1.39 (0.80) | 1.79 (0.85)/1.72 (0.89) | 0.918/0.256 |

## References

- Abdalah, M.Q. Gender Difference in Learning Disabled Children Neuropsychological Review. Res. Rev. Healthc. Open Access J.
**2018**, 1. [Google Scholar] [CrossRef] - American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association, Ed.; American Psychiatric Publishing: Arlington, VA, USA, 2013. [Google Scholar]
- Bosch-Bayard, J.; Peluso, V.; Galan, L.; Valdes Sosa, P.; Chiarenza, G. Clinical and Electrophysiological Differences between Subjects with Dysphonetic Dyslexia and Non-Specific Reading Delay. Brain Sci.
**2018**, 8, 172. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fernandez, T.; Harmony, T.; Bosch-Bayard, J.; Prado-Alcalá, R.; Otero-Ojeda, G.; Garcia, F.; Rodriguez, M.D.C.; Becerra, J. Optimization of the Neurofeedback protocol in children with Learning Disabilities and a lag in their EEG maturation. Front. Hum. Neurosci.
**2015**, 9. [Google Scholar] [CrossRef] - Chiarenza, G.A. Quantitative EEG in Childhood Attention Deficit Hyperactivity Disorder and Learning Disabilities. Clin. EEG Neurosci.
**2020**, 155005942096234. [Google Scholar] [CrossRef] [PubMed] - Fernández, T.; Harmony, T.; Fernández-Bouzas, A.; Silva, J.; Herrera, W.; Santiago-Rodríguez, E.; Sánchez, L. Sources of EEG activity in learning disabled children. Clin. EEG Electroencephalogr.
**2002**, 33, 160–164. [Google Scholar] [CrossRef] [PubMed] - Gasser, T.; Rousson, V.; Schreiter Gasser, U. EEG power and coherence in children with educational problems. J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc.
**2003**, 20, 273–282. [Google Scholar] [CrossRef] [PubMed] - Harmony, T.; Marosi, E.; Díaz de León, A.E.; Becker, J.; Fernández, T. Effect of sex, psychosocial disadvantages and biological risk factors on EEG maturation. Electroencephalogr. Clin. Neurophysiol.
**1990**, 75, 482–491. [Google Scholar] [CrossRef] - John, E.R.; Prichep, L.; Ahn, H.; Easton, P.; Fridman, J.; Kaye, H. Neurometric evaluation of cognitive dysfunctions and neurological disorders in children. Prog. Neurobiol.
**1983**, 21, 239–290. [Google Scholar] [CrossRef] - Roca-Stappung, M.; Fernández, T.; Bosch-Bayard, J.; Harmony, T.; Ricardo-Garcell, J. Electroencephalographic characterization of subgroups of children with learning disorders. PLoS ONE
**2017**, 12, e0179556. [Google Scholar] [CrossRef] [Green Version] - Angelakis, E.; Stathopoulou, S.; Frymiare, J.L.; Green, D.L.; Lubar, J.F.; Kounios, J. EEG Neurofeedback: A Brief Overview and an Example of Peak Alpha Frequency Training for Cognitive Enhancement in the Elderly. Clin. Neuropsychol.
**2007**, 21, 110–129. [Google Scholar] [CrossRef] - Dickinson, A.; DiStefano, C.; Senturk, D.; Jeste, S.S. Peak alpha frequency is a neural marker of cognitive function across the autism spectrum. Eur. J. Neurosci.
**2018**, 47, 643–651. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res. Rev.
**1999**, 29, 169–195. [Google Scholar] [CrossRef] - Suldo, S.M.; Olson, L.A.; Evans, J.R. Quantitative EEG Evidence of Increased Alpha Peak Frequency in Children with Precocious Reading Ability. J. Neurother.
**2002**, 5, 39–50. [Google Scholar] [CrossRef] [Green Version] - Demos, J.N. Getting Started with EEG Neurofeedback, 2nd ed.; W.W. Norton & Company: New York, NY, USA, 2019; ISBN 978-0-393-71253-7. [Google Scholar]
- Blum, A.S.; Rutkove, S.B. (Eds.) The Clinical Neurophysiology Primer; Humana Press: Totowa, NJ, USA, 2007; ISBN 978-0-89603-996-4. [Google Scholar]
- Bazanova, O.M. Alpha EEG Activity Depends on the Individual Dominant Rhythm Frequency. J. Neurother.
**2012**, 16, 270–284. [Google Scholar] [CrossRef] - Arns, M.; Drinkenburg, W.H.; Fitzgerald, P.B.; Kenemans, J.L. Neurophysiological predictors of non-response to rTMS in depression. Brain Stimulat.
**2012**, 5, 569–576. [Google Scholar] [CrossRef] [PubMed] - Grandy, T.H.; Werkle-Bergner, M.; Chicherio, C.; Lövdén, M.; Schmiedek, F.; Lindenberger, U. Individual alpha peak frequency is related to latent factors of general cognitive abilities. NeuroImage
**2013**, 79, 10–18. [Google Scholar] [CrossRef] [Green Version] - Niedermeyer, E.; Lopes da Silva, F.H. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th ed.; Lippincott Williams & Wilkins: London, UK, 2005; ISBN 978-0-7817-5126-1. [Google Scholar]
- Fernández, T.; Bosch-Bayard, J.; Harmony, T.; Caballero, M.I.; Díaz-Comas, L.; Galán, L.; Ricardo-Garcell, J.; Aubert, E.; Otero-Ojeda, G. Neurofeedback in Learning Disabled Children: Visual versus Auditory Reinforcement. Appl. Psychophysiol. Biofeedback
**2016**, 41, 27–37. [Google Scholar] [CrossRef] [PubMed] - Arns, M. EEG-Based Personalized Medicine in ADHD: Individual Alpha Peak Frequency as an Endophenotype Associated with Nonresponse. J. Neurother.
**2012**, 16, 123–141. [Google Scholar] [CrossRef] - Carrobles, J.A. Bio/neurofeedback. Clin. Salud
**2016**, 27, 125–131. [Google Scholar] [CrossRef] [Green Version] - Groeneveld, K.M.; Mennenga, A.M.; Heidelberg, R.C.; Martin, R.E.; Tittle, R.K.; Meeuwsen, K.D.; Walker, L.A.; White, E.K. Z-Score neurofeedback and heart rate variability training for adults and children with symptoms of Attention-Deficit/Hyperactivity Disorder: A retrospective study. Appl. Psychophysiol. Biofeedback
**2019**, 44, 291–308. [Google Scholar] [CrossRef] [Green Version] - Alkoby, O.; Abu-Rmileh, A.; Shriki, O.; Todder, D. Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning. Neuroscience
**2018**, 378, 155–164. [Google Scholar] [CrossRef] [PubMed] - Doehnert, M.; Brandeis, D.; Straub, M.; Steinhausen, H.-C.; Drechsler, R. Slow cortical potential neurofeedback in attention deficit hyperactivity disorder: Is there neurophysiological evidence for specific effects? J. Neural Transm.
**2008**, 115, 1445–1456. [Google Scholar] [CrossRef] [PubMed] - Hanslmayr, S.; Klimesch, W.; Sauseng, P.; Gruber, W.; Doppelmayr, M.; Freunberger, R.; Pecherstorfer, T. Visual discrimination performance is related to decreased alpha amplitude but increased phase locking. Neurosci. Lett.
**2005**, 375, 64–68. [Google Scholar] [CrossRef] [PubMed] - Lubar, J.F.; Swartwood, M.O.; Swartwood, J.N.; O’Donnell, P.H. Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance. Biofeedback Self-Regul.
**1995**, 20, 83–99. [Google Scholar] [CrossRef] [PubMed] - Weber, E.; Köberl, A.; Frank, S.; Doppelmayr, M. Predicting Successful Learning of SMR Neurofeedback in Healthy Participants: Methodological Considerations. Appl. Psychophysiol. Biofeedback
**2011**, 36, 37–45. [Google Scholar] [CrossRef] - Zoefel, B.; Huster, R.J.; Herrmann, C.S. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. NeuroImage
**2011**, 54, 1427–1431. [Google Scholar] [CrossRef] - Collura, T. Technical Foundations of Neurofeedback; Routledge, Taylor & Francis Group: New York, NY, USA, 2014; ISBN 978-0-415-89901-7. [Google Scholar]
- Collura, T. Handbook of Clinical QEEG and Neurotherapy, 1st ed.; Includes bibliographical references and index; Routledge: New York, NY, USA, 2016; ISBN 978-1-315-75409-3. [Google Scholar]
- Collura, T.; Guan, J.; Tarrant, J.; Bailey, J.; Starr, F. EEG biofeedback case studies using live Z-score training and a normative database. J. Neurother.
**2010**, 14, 22–46. [Google Scholar] [CrossRef] [Green Version] - Smith, M.L. A father finds a solution: Z-Score Training. NeuroConnections
**2008**, 22–25. Available online: https://brainmaster.com/wp-content/uploads/2016/03/smith-nc.pdf (accessed on 28 January 2021). - Thatcher, R.W. Handbook of Quantitative Electroencephalography and EEG Biofeedback, 2nd ed.; Anipublishing Co.: St. Petersburg, FL, USA, 2016; ISBN 978-0-9854692-0-7. [Google Scholar]
- Thatcher, R.W.; Lubar, J.F.; Koberda, J.L. Z-Score EEG Biofeedback: Past, Present, and Future. Biofeedback
**2019**, 47, 89–103. [Google Scholar] [CrossRef] - Krigbaum, G.; Wigton, N.L. When discussing neurofeedback, does modality matter? NeuroRegulation
**2014**, 1, 48–60. [Google Scholar] [CrossRef] - Krigbaum, G.; Wigton, N.L. A methodology of analysis for monitoring treatment progression with 19-Channel Z-Score neurofeedback (19ZNF) in a single-subject design. Appl. Psychophysiol. Biofeedback
**2015**, 40, 139–149. [Google Scholar] [CrossRef] - Wigton, N.L.; Krigbaum, G. Attention, executive function, behavior, and electrocortical function, significantly improved with 19-Channel Z -Score Neurofeedback in a Clinical Setting: A Pilot Study. J. Atten. Disord.
**2015**, 23, 398–408. [Google Scholar] [CrossRef] [PubMed] - Szewczyk, R.Ł.; Ratomska, M.; Jaśkiewicz, M. The Neglected Problem of the Neurofeedback Learning (In) Ability. In Biomedical Engineering and Neuroscience; Hunek, W.P., Paszkiel, S., Eds.; Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Switzerland, 2018; Volume 720, pp. 45–58. ISBN 978-3-319-75024-8. [Google Scholar]
- Burde, W.; Blankertz, B. Is the locus of control of reinforcement a predictor of brain-computer interface performance? In Proceedings of the International Brain-Computer Interface Workshop and Training Course, Graz, Austria, 30 May–3 June 2006; Graz University of Technology: Graz, Austria, 2006; pp. 76–77. [Google Scholar]
- Daum, I.; Rockstroh, B.; Birbaumer, N.; Elbert, T.; Canavan, A.; Lutzenberger, W. Behavioural treatment of slow cortical potentials in intractable epilepsy: Neuropsychological predictors of outcome. J. Neurol. Neurosurg. Psychiatry
**1993**, 56, 94–97. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Gruzelier, J.H. EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants. Neurosci. Biobehav. Rev.
**2014**, 44, 124–141. [Google Scholar] [CrossRef] [PubMed] - Kouijzer, M.E.J.; van Schie, H.T.; Gerrits, B.J.L.; Buitelaar, J.K.; de Moor, J.M.H. Is EEG-biofeedback an Effective Treatment in Autism Spectrum Disorders? A Randomized Controlled Trial. Appl. Psychophysiol. Biofeedback
**2013**, 38, 17–28. [Google Scholar] [CrossRef] [PubMed] - Roberts, L.E.; Birbaumer, N.; Rockstroh, B.; Lutzenberger, W.; Elbert, T. Self-Report During Feedback Regulation of Slow Cortical Potentials. Psychophysiology
**1989**, 26, 392–403. [Google Scholar] [CrossRef] - Wangler, S.; Gevensleben, H.; Albrecht, B.; Studer, P.; Rothenberger, A.; Moll, G.H.; Heinrich, H. Neurofeedback in children with ADHD: Specific event-related potential findings of a randomized controlled trial. Clin. Neurophysiol.
**2011**, 122, 942–950. [Google Scholar] [CrossRef] - Jafarova, O.; Mazhirina, K.; Sokhadze, E.; Shtark, M. Self-regulation Strategies and Heart Rate Biofeedback Training. Appl. Psychophysiol. Biofeedback
**2020**, 45, 87–98. [Google Scholar] [CrossRef] - Blankertz, B.; Sannelli, C.; Halder, S.; Hammer, E.M.; Kübler, A.; Müller, K.-R.; Curio, G.; Dickhaus, T. Neurophysiological predictor of SMR-based BCI performance. NeuroImage
**2010**, 51, 1303–1309. [Google Scholar] [CrossRef] [Green Version] - Grosse-Wentrup, M.; Schölkopf, B. High gamma-power predicts performance in sensorimotor-rhythm brain–computer interfaces. J. Neural Eng.
**2012**, 9, 1–8. [Google Scholar] [CrossRef] - Cantor, D.S.; Chabot, R. QEEG Studies in the Assessment and Treatment of Childhood Disorders. Clin. EEG Neurosci.
**2009**, 40, 113–121. [Google Scholar] [CrossRef] - Holmes, G.L.; Solomon, M.; Royden, J. Clinical Neurophysiology of Infancy, Childhood, and Adolescence; Butterworth Heinemnn Elsevier: Philadelphia, PA, USA, 2006; ISBN 0-7506-7251-X. [Google Scholar]
- López-Ibor Aliño, J.J.; Valdés Miyar, M.; American Psychiatric Association. Manual Diagnóstico y Estadístico de los Trastornos Mentales; American Psychiatric Pub.: Washington, DC, USA, 2003; ISBN 978-84-458-1087-3. [Google Scholar]
- Kaufman, A.S.; Flanagan, D.P.; Alfonso, V.C.; Mascolo, J.T. Test Review: Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV). J. Psychoeduc. Assess.
**2006**, 24, 278–295. [Google Scholar] [CrossRef] - Soutar, R.G. Holistic Neurointegration: The New Mind Model—A Bio-Psycho-Social qEEG Guided Neurofeedback Method; New Mind Academy: Roswell, GA, USA, 2018. [Google Scholar]
- Stoller, L. Z-Score Training, Combinatorics, and Phase Transitions. J. Neurother.
**2011**, 15, 35–53. [Google Scholar] [CrossRef] [Green Version] - Arns, M.; Gunkelman, J.; Breteler, M.; Spronk, D. EEG phenotipes predict treatment outcome to stimulants in children with ADHD. J. Integr. Neurosci.
**2008**, 7, 421–438. [Google Scholar] [CrossRef] - Rubin, D.I.; Daube, J.R. Clinical Neurophysiology, 4th ed.; Oxford University Press: Oxford, UK, 2016; ISBN 978-0-19-025963-1. [Google Scholar]
- Pérez-Elvira, R.; Oltra-Cucarella, J.; Carrobles, J.A. Effects of QEEG normalization using 4-Channel Live Z-Score Training Neurofeedback for children with learning disabilities: Preliminary data. Behav. Psychol
**2021**, in press. [Google Scholar] - Pérez-Elvira, R.; Oltra-Cucarella, J.; Carrobles, J.A. Comparing Live Z-Score Training and Theta/Beta Protocol to Reduce Theta-to-Beta Ratio: A Pilot Study. NeuroRegulation
**2020**, 7, 58. [Google Scholar] [CrossRef] - Fisher, W.; Piazza, C.C.; Bowman, L.G.; Hagopian, L.P.; Owens, J.C.; Slevin, I. A comparison of two approaches for identifying reinforcers for persons with severe and profound disabilities. J. Appl. Behav. Anal.
**1992**, 25, 491–498. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mangum, A.; Fredrick, L.; Pabico, R.; Roane, H. The role of context in the evaluation of reinforcer efficacy: Implications for the preference assessment outcomes. Res. Autism Spectr. Disord.
**2012**, 6, 158–167. [Google Scholar] [CrossRef] [Green Version] - Piazza, C.C.; Fisher, W.W.; Hagopian, L.P.; Bowman, L.G.; Toole, L. Using a choice assessment to predict reinforcer effectiveness. J. Appl. Behav. Anal.
**1996**, 29, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Höller, Y.; Thomschewski, A.; Schmid, E.V.; Höller, P.; Crone, J.S.; Trinka, E. Individual brain-frequency responses to self-selected music. Int. J. Psychophysiol.
**2012**, 86, 206–213. [Google Scholar] [CrossRef] - Agresti, A. Categorical Data Analysis, 2nd ed.; Wiley series in probability and statistics; Wiley-Interscience: New York, NY, USA, 2002; ISBN 978-0-471-36093-3. [Google Scholar]
- Vittinghoff, E.; Glidden, D.V.; Shiboski, S.C.; McCulloch, C.E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models; Statistics for Biology and Health; Springer: New York, NY, USA, 2005. [Google Scholar]
- Andreou, C.; Frielinghaus, H.; Rauh, J.; Mußmann, M.; Vauth, S.; Braun, P.; Leicht, G.; Mulert, C. Theta and high-beta networks for feedback processing: A simultaneous EEG–fMRI study in healthy male subjects. Transl. Psychiatry
**2017**, 7, e1016. [Google Scholar] [CrossRef] - Güntensperger, D.; Thüring, C.; Kleinjung, T.; Neff, P.; Meyer, M. Investigating the Efficacy of an Individualized Alpha/Delta Neurofeedback Protocol in the Treatment of Chronic Tinnitus. Neural Plast.
**2019**, 2019, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Arns, M.; Spronk, D.; Fitzgerald, P.B. Potential differential effects of 9 Hz rTMS and 10 Hz rTMS in the treatment of depression. Brain Stimulat.
**2010**, 3, 124–126. [Google Scholar] [CrossRef] [PubMed] - Bazanova, O.M.; Aftanas, L.I. Individual EEG Alpha Activity Analysis for Enhancement Neurofeedback Efficiency: Two Case Studies. J. Neurother.
**2010**, 14, 244–253. [Google Scholar] [CrossRef] [Green Version] - Kropotov, J. Methods of Neurotherapy. In Quantitative EEG, Event-Related Potentials and Neurotherapy; Elsevier: Amsterdam, The Netherlands, 2009; pp. 469–505. ISBN 978-0-12-374512-5. [Google Scholar]
- Kropotov, J. Functional Neuromarkers for Psychiatry; Elsevier: Boston, MA, USA, 2016; ISBN 978-0-12-410513-3. [Google Scholar]
- Johnstone, J.; Gunkelman, J. Use of Databases in QEEG Evaluation. J. Neurother.
**2003**, 7, 31–52. [Google Scholar] [CrossRef] [Green Version] - Pérez-Elvira, R.; López Bote, D.J.; Guarino, S.; Agudo Juan, M.; De León, R.J.; Feiner, T.; Perez, B. Neurometric results of a case series using Live Z-Scores neurofeedback. Int. J. Psychophysiol.
**2018**, 131, S139–S140. [Google Scholar] [CrossRef] - Pérez-Elvira, R.; Carrobles, J.; López Bote, D.; Oltra-Cucarella, J. Efficacy of Live Z-Score neurofeedback training for chronic insomnia: A single-case study. NeuroRegulation
**2019**, 6, 93–101. [Google Scholar] [CrossRef] - Azizi, A.; Drikvand, F.M.; Sepahvandi, M.A. Comparison of the Effect of Cognitive Rehabilitation and Neurofeedback on Sustained Attention Among Elementary School Students with Specific Learning Disorder: A Preliminary Randomized Controlled Clinical Trial. Appl. Psychophysiol. Biofeedback
**2018**, 43, 301–307. [Google Scholar] [CrossRef] - Duarte Hernández, E.; González Marqués, J.; Alvarado, J.M. Effect of the Theta-Beta Neurofeedback Protocol as a Function of Subtype in Children Diagnosed with Attention Deficit Hyperactivity Disorder. Span. J. Psychol.
**2016**, 19, E30. [Google Scholar] [CrossRef] [Green Version] - Hillard, B.; El-Baz, A.S.; Sears, L.; Tasman, A.; Sokhadze, E.M. Neurofeedback Training Aimed to Improve Focused Attention and Alertness in Children With ADHD: A Study of Relative Power of EEG Rhythms Using Custom-Made Software Application. Clin. EEG Neurosci.
**2013**, 44, 193–202. [Google Scholar] [CrossRef] - Weber, L.A.; Ethofer, T.; Ehlis, A.-C. Predictors of neurofeedback training outcome: A systematic review. NeuroImage Clin.
**2020**, 27, 102301. [Google Scholar] [CrossRef] - Krepel, N.; Egtberts, T.; Sack, A.T.; Heinrich, H.; Ryan, M.; Arns, M. A multicenter effectiveness trial of QEEG-informed neurofeedback in ADHD: Replication and treatment prediction. NeuroImage Clin.
**2020**, 28, 102399. [Google Scholar] [CrossRef] - Martínez-Briones, B.J.; Fernández-Harmony, T.; Garófalo Gómez, N.; Biscay-Lirio, R.J.; Bosch-Bayard, J. Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis. Brain Sci.
**2020**, 10, 817. [Google Scholar] [CrossRef] [PubMed] - Breteler, M.H.M.; Arns, M.; Peters, S.; Giepmans, I.; Verhoeven, L. Improvements in Spelling after QEEG-based Neurofeedback in Dyslexia: A Randomized Controlled Treatment Study. Appl. Psychophysiol. Biofeedback
**2010**, 35, 5–11. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Koberda, J.L.; Moses, A.; Koberda, L.; Koberda, P. Cognitive Enhancement Using 19-Electrode Z -Score Neurofeedback. J. Neurother.
**2012**, 16, 224–230. [Google Scholar] [CrossRef] [Green Version] - Wigton, N.L. Clinical perspectives of 19-Channel Z-Score neurofeedback: Benefits and limitations. J. Neurother.
**2013**, 17, 259–264. [Google Scholar] [CrossRef] - Miglioretti, D.L.; Heagerty, P.J. Marginal Modeling of Nonnested Multilevel Data using Standard Software. Am. J. Epidemiol.
**2006**, 165, 453–463. [Google Scholar] [CrossRef] [Green Version] - Akter, T.; Sarker, E.B.; Rahman, S. A Tutorial on GEE with Applications to Diabetes and Hypertension Data from a Complex Survey. J. Biomed. Anal.
**2018**, 1, 37–50. [Google Scholar] [CrossRef]

**Figure 1.**Enrollment criteria: I, first quantitative EEG (QEEG) to evaluate abnormal patterns vs. database norms and to compute out-of-the-range (±1.5 SD) waves number and Cognitive and Emotional Checklist (CEC) score values pre-LZT-NF (Live Z-Score Training Neurofeedback) sessions; II, li-APF (low individual alpha peak) and ni-APF (normal individual alpha peak) subgroup designation based on a 9.5 Hz cutoff point for i-APF (individual alpha peak visually identified in the spectrogram); III, 10 LZT-NF sessions (30 min each) with real-time (RT) Z-scores vs. database norms to constrain within the range (±1.5 SD) the abnormal waves; IV, second QEEG to evaluate abnormal patterns vs. database norms and to compute out-of-the-range (±1.5 SD) waves number and CEC score values post-LZT-NF sessions; V, statistical analysis in li-APF subjects regarding out-of-the-range waves number (±1.5 SD), before and after employing GEE (generalized estimating equation), and CEC scores using repeated measures ANOVA. LD: learning disabilities, FFT: Fast Fourier Transform, JTFA: Join Time Frequency Analysis, PZOKUL: BrainMaster protocol Percentage of Z-Scores OK Upper and Lower thresholds.

**Figure 2.**A visual representation of the study methods: participants’ EEG was measured with a 19-channel amplifier and a Linked Ears montage pre- and post-LZT-NF intervention (

**A**–

**C**). EEG from all 19 channels were imported and visually edited in NeuroGuide to remove artifacts (green circle) (

**B**), and the fast Fourier transform converted the signal into frequency-based measures of absolute power and Z-scores (

**C**,

**D**). Participants’ parents/tutors filled the CEC both pre- and post-LZT-NF intervention (

**E**). Participants were then divided into li-APF and ni-APF based on i-APF spectrogram pre-LZT-NF intervention (

**F**–

**H**). Post-LZT-NF intervention, repeated measures ANOVA and binary logistic regression analyzed the difference between ni-APF and li-APF groups to identify optimal responders (

**I**–

**K**).

**Figure 3.**(

**a**) Spectrogram showing the absolute amplitude peak representing the i-APF of the subject. Based on the cutoff point, these data (8.50 Hz) correspond to a participant with a low i-APF (in the right occipital, O

_{2}, from the 19-channel spectrogram). On the x-axis, the frequency is expressed in Hz, and on the y-axis, the wave’s absolute amplitude is expressed in microvolts (uV). (

**b**) The same spectrogram shows the absolute amplitude peak with 10 Hz for a participant with a normal i-APF (the same right occipital, O

_{2}, channel). The abscissa and the ordinate parameters are similar to those presented in Figure 3a.

**Figure 4.**(

**a**) Subject with F3, F4, P3, and P4 locations selected for LZT-NF protocol (PZOKUL) and LE montage. (

**b**) Real-time EEG records from the four leads with Join Time Frequency Analysis (JTFA). (

**c**) Z-scores (using JTFA) computed in real time. (

**d**) %Z absolute power within ±1.5 SD. (

**f**) If the %Z absolute power is within the range, then the display shows a movie with a clear image (1). (

**e**) If the %Z absolute power is out of the range, then the display shows a movie with a dimmer that darkens the image (2).

**Figure 5.**Venn diagram of the frequencies of cognitive impairments found across the whole study group: 2 children showed impairments in all of the three skills (reading, writing, and arithmetic), 6 had impairments in reading and arithmetic, 6 had reading and writing impairments, and 2 were impaired in writing and arithmetic.

**Figure 7.**The map of a li-APF group subject’s (

**A**) and a ni-APF group subject’s (

**B**) pre (top)- and post (bottom)-intervention Z-scores. It can be seen how far each frequency band deviates from the norm (−1.5, +1.5 Z-scores) (the color scale for −3/+3 Z-scores under the maps indicates the deviations and whether they are positive or negative). An improvement in beta activity can be observed.

**Figure 8.**Pre-/post-intervention scores for cognitive and emotional tests (CEC-Total scores). The dotted line illustrates the results for the ni-APF group, and the continuous line represents the results for the li-APF group. Both groups were similar pre-intervention, but the ni-APF group achieved better results, further reducing the total scores in the CEC (with higher scores indicating problems of greater frequency and severity). Note that the initial scores for both groups overlap, but the final results do not.

**Figure 9.**Pre-/post-intervention scores for cognitive and emotional test learning items. The results for the ni-APF group are illustrated with the dotted line, and the continuous line shows the results for the li-APF group. Both groups were similar pre-intervention, but the ni-APF group achieved better results, further reducing the learning scores in the CEC (with higher scores indicating problems of greater frequency and severity). As for the total scores, the initial scores for both groups overlap, but the post-treatment scores do not.

**Table 1.**Numbers of waves out of the normal range for the absolute power Z-scores (in absolute values) by group.

Low i-APF Group (li-APF, n = 12) | Normal i-APF Group (ni-APF, n = 28) | |||
---|---|---|---|---|

Waves | Pre | Post | Pre | Post |

Abs Z < 1.5 | 257 (76.49%) | 246 (73.21%) | 519 (66.19%) | 662 (84.44%) |

Abs Z ≥ 1.5 | 79 (23.51%) | 90 (26.79%) | 265 (33.81%) | 122 (15.56%) |

Total | 336 | 336 | 784 | 784 |

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**MDPI and ACS Style**

Pérez-Elvira, R.; Oltra-Cucarella, J.; Carrobles, J.A.; Teodoru, M.; Bacila, C.; Neamtu, B.
Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities. *Brain Sci.* **2021**, *11*, 167.
https://doi.org/10.3390/brainsci11020167

**AMA Style**

Pérez-Elvira R, Oltra-Cucarella J, Carrobles JA, Teodoru M, Bacila C, Neamtu B.
Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities. *Brain Sciences*. 2021; 11(2):167.
https://doi.org/10.3390/brainsci11020167

**Chicago/Turabian Style**

Pérez-Elvira, Rubén, Javier Oltra-Cucarella, José Antonio Carrobles, Minodora Teodoru, Ciprian Bacila, and Bogdan Neamtu.
2021. "Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities" *Brain Sciences* 11, no. 2: 167.
https://doi.org/10.3390/brainsci11020167