Event Abstract

Back to Event

Individualized neurofeedback training for ADHD children

Olga M. Bazanova1* and Elena A. Sapina1*
  • 1 Institute od Physiology and Basic Medicine, Russia

BACKGROUND Inattention, impulsivity and hyperactivity characterizing ADHD are associated with enhancement amplitude of low frequency range and decreasing amplitude of SMR or low beta EEG oscillations (Lubar et al., 1995). It is the main reason why theta/ beta ratio (TBR) is traditionally used in neurofeedback training (NFT) protocols to treat children with ADHD (Monastra et al., 2005). Still its efficacy is debatable (Cortese et al., 2016). The most probable cause NFT low efficacy level is the absence of individualization in detection of target frequency ranges (Bazanova & Aftanas, 2010). In D.Kaiser (2001) and later in Bazanova and Aftanas (2006) stusies it was shown that taking into account individually determined EEG alpha band could improve efficiency of NFT for ADHD. (Bazanova & Aftanas, 2010). Moreover later it was recognized that EEG mean frequency in children with ADHD is slowed down in comparison with healthy peers (Rudo-Hutt, 2014). Both facts reveal the idea that traditional EEG frequency band cannot be applied to ADHD EEG data analysis and NFT application. Consequently theta and beta ranges should be detected according to alpha band. Alpha band width in its turn could be defined easily due to frequency range where Berger effect appears (Bazanova & Vernon 2014). The question arises if individual EEG frequencies used in NFT will change the treatment efficiency. The aim of this study was to refine NFT protocol for ADHD treatment. METHODS Sixty two boys aged 6-9 diagnosed with ADHD according to DSM-V from waiting-list were randomly assigned to either individual NFT group (n = 31), standard NFT group (n = 17) or in control group with mock NFT (n=14). Children with comorbid disorders were excluded. Pre- and post-treatment assessment consisted of psychometric measures, behavioral rating scales completed by parents and teachers, as well as psychophysiological measures. Psychometric tests included Go/no-Go task (Avila et al., 2004) and delayed gratification test (Shoda et al., 1990). Behavioral rating scales consisted of parents and teachers interview and SNAP-IV (Swanson et al., 2013). Electroencephalogram (EEG) and electromyogram (EMG) registered simultaneously in eyes closed (1 min) and eyes opened (1 min) rest condition with 19 monopolar electrodes according to “10-20” system.. Individual alpha peak frequency (IAPF) and alpha frequency band boundaries were determined individually for every particular subject as previously was described (Bazanova &Aftanas, 2010). These measurements were used for training in individual NFT (iNFT) group. For standard NFT (sNFT) group frequency bands were used in training as follows: 8-12 Hz (alpha), 4-7,5 (theta) a 12,5-20 (beta). (Recommendations for the Practice of Clinical Neurophysiology, 1999). Treatment for both experimental groups consisted of 10 sixteen min theta/beta ratio (TBR) decreasing sessions. In iNFT group TBR were calculated for every subject basing on their individual frequencies, in sNFT group TBR based on standard frequency ranges. EMG registration was used to eliminate EEG contamination since it is very probable in alpha and beta frequency bands (Goncharova, 2003) and to decrease forehead muscle tonus (Masalski et al., 2013). Control group trainings consisted of 10 mock NFT sessions sixteen min each. After six month children in all groups were retested. ANOVA was used to analyze psychometric, behavior and psychophysiological variables. RESULTS Behavioural traits before trainings showed no difference between groups. Meanwhile after ten sessions significant difference was found between iNFT group and both sNFT and mock NFT group in attention and impulse control test (р = from 0,01 to 0,001). These post training improvement in attention and decreasing impulsivity are supported by showed significant increasing iAPF, enchantment alpha 2 band width and power, decreasing the alpha 1/ alpha 2 ratio (р≤0,001). According to our findings, the difference between the three NFT groups could be observed in the alpha-2 frequency band and was most apparent in the posterior sites in iNFT group where higher power was seen after six month. CONCLUSION The results prove that individualized NFT is significantly more efficient than standard NFT protocol in providing the attention and impulse control improvement.

Acknowledgements

The study was supported by RHF grant # 14-06-00951a

References

1. Avila C, Cuenca I, Felix V, Parcet MA, Miranda A. Measuring impulsivity in school-aged boys and examining its relationship with ADHD and ODD ratings. J Abnorm Child Psychol. 2004;32(3):295–304.
2. Bazanova O., Aftanas L. I. Individual EEG Alpha Activity Analysis for Enhancement Neurofeedback Efficiency: Two Case Studies. Journal of Neurotherapy (2010),14(3):244-253
3. Bazanova O., Aftanas L. I. Relationships between learnability and individual indices of EEG alpha activity. Journal Annals of General Psychiatry, 2006, V.5, 1.
4. Bazanova O.M., Vernon D. Interpreting EEG alpha activity. Neurosci Biobehav Rev. 2014 Jul; Epub 2013.
5. Cortese S., Ferrin M., Brandeis D., Holtmann M., Aggensteiner P., Daley D., Santosh P., Simonoff P., Stevenson J., Stringaris A., Sonuga-Barke E.J. (2016) Neurofeedback for attention deficit/ hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. Journal of American academy child and adolescent psychiatry, 444, 55.
6. Goncharova I.I., McFarland D.J., Vaughan T.M., Wolpaw J.R. (2003) EMG contamination of EEG: spectral and topographical characteristics. Clinical Neurophysiology 114 (2003) 1580–1593.
7. Kaiser, D. A. (2001). Rethinking standard bands. Journal of Neurotherapy, 5, 87–96.
8. 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 TOVA scores, behavioral ratings, and WISC-R performance. Biofeedback and Self-Regulation, (1995) 20, 83-99.
9. Masalski M., Banas B., Krecicki T. (2013) The influence of eyeball rotation on the results of auditory steady-state responses. Adv Clin Exp Med. (2013) 22(4):549-54.
10. Monastra V. J. Electroencephalographic biofeedback (neurotherapy) as a treatment for attention deficit hyperactivity disorder: rationale and empirical foundation. Child Adolesc Psychiatric Clin N Am 14 (2005) 55 – 82.
11. Recommendations for the Practice of Clinical Neurophysiology: Guidelines of the International Federation of Clinical Physiology (EEG Suppl. 52) (1999) Editors: Deuschl G. and Eisen A. International Federation of Clinical Neurophysiology. Published by Elsevier Science B.V.
12. Rudo-Hutt A.S. Electroencephalography and externalizing behavior: a meta-analysis. Biol Psychol. 2015, Epub 2014.
13. Shoda Y., Mischel W., Peake P. (1990) Predicting adolescent cognitive and self-regulatory competences from preschool delay of gratification: identifying diagnostic conditions. Developmental psychology, vol. 26, No. 6, 978-986.
14. Swanson, Nolan and Pelham Teacher and Parent Rating Scale (SNAP-IV) et al, 2013 TDMHSAS Research Team, P. 454.

Keywords: Neurofeedback Training, ADHD, individual alpha peak frequency, individual alpha band, standard frequency ranges

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral Presentation in SAN 2016 Conference

Topic: Workshop on Individualized Neurofeedback

Citation: Bazanova OM and Sapina EA (2016). Individualized neurofeedback training for ADHD children. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00061

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 29 Jul 2016; Published Online: 01 Aug 2016.

* Correspondence:
Prof. Olga M Bazanova, Institute od Physiology and Basic Medicine, Novosibirsk, 630117, Russia, bazanova_olgamih@mail.ru
Mrs. Elena A Sapina, Institute od Physiology and Basic Medicine, Novosibirsk, 630117, Russia, sapina.elena@gmail.com