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Clinical Efficacy of a New Automated Hemoencefalographic Neurofeedback Protocol

Published online by Cambridge University Press:  10 January 2013

Álvaro Machado Dias ,
Adrian Machado Van Deusen ,
Eduardo Oda  and
Mariana Rodrigues Bonfim
Álvaro Machado Dias*
Affiliation:
Universidade Federal de Säo Paulo (Brazil)
Adrian Machado Van Deusen
Affiliation:
Itallis Communication Llc (USA)
Eduardo Oda
Affiliation:
Universidade de Säo Paulo (Brazil)
Mariana Rodrigues Bonfim
Affiliation:
Clínica BIOS Saúde (Brazil)
*
Correspondence concerning this article should be addressed to Alvaro Machado Dias. Institute of Psychiatry, University of São Paulo Medical School. Rua Dr. Ovídio Pires Campos s/n, São Paulo (Brazil). Email: [email protected]
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Abstract

Among the ongoing attempts to enhance cognitive performance, an emergent and yet underrepresented venue is brought by hemoencefalographic neurofeedback (HEG). This paper presents three related advances in HEG neurofeedback for cognitive enhancement: a) a new HEG protocol for cognitive enhancement, as well as b) the results of independent measures of biological efficacy (EEG brain maps) extracted in three phases, during a one year follow up case study; c) the results of the first controlled clinical trial of HEG, designed to assess the efficacy of the technique for cognitive enhancement of an adult and neurologically intact population. The new protocol was developed in the environment of a software that organizes digital signal algorithms in a flowchart format. Brain maps were produced through 10 brain recordings. The clinical trial used a working memory test as its independent measure of achievement. The main conclusion of this study is that the technique appears to be clinically promising. Approaches to cognitive performance from a metabolic viewpoint should be explored further. However, it is particularly important to note that, to our knowledge, this is the world's first controlled clinical study on the matter and it is still early for an ultimate evaluation of the technique.

Entre los intentos en curso para mejorar el rendimiento cognitivo, uno emergente y todavía insuficientemente representado es el neurofeedback hemoencefalográphico (HEG). Este trabajo presenta tres avances relacionados con HEG neurofeedback para la mejora cognitiva: a) un nuevo protocolo HEG para la mejora cognitiva, así como b) los resultados de las medidas independientes de la eficacia biológica (mapas cerebrales EEG) extraídos en tres fases durante un año estudio de seguimiento de casos; c) los resultados del primer ensayo clínico controlado de HEG, diseñado para evaluar la eficacia de la técnica para la mejora cognitiva de población adulta y neurológicamente sana. El nuevo protocolo fue desarrollado en el marco de un software que organiza algoritmos de señales digitales en un formato de diagrama de flujo. Los mapas de cerebro fueron producidos a través de 10 registros cerebrales. El ensayo clínico utilizó un test de memoria de trabajo como medida independiente de sus logros. La principal conclusión de este estudio es que la técnica parece ser clínicamente prometedora. Los enfoques para el rendimiento cognitivo desde un punto de vista metabólico deben investigarse más a fondo. Sin embargo, es particularmente importante tener en cuenta que, a nuestro entender, este es el primer estudio clínico controlado sobre el tema en el mundo, y aún es pronto para una evaluación final de la técnica.

Keywords

HEG neurofeedbackclinical trialcognitive reservebrain metabolismcognitive performanceneurofeedback HEGensayo clínicoreserva cognitivametabolismo cerebralrendimiento cognitivo
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 15 , Issue 3 , November 2012 , pp. 930 - 941
Copyright
Copyright © Cambridge University Press 2012

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References

Angelakis, E., Stathopoulou, S., Frymiare, J. L., Green, D. L., Lubar, J. F., & Kounios, J. (2007). EEG neurofeedback: A brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clinical Neuropsychology, 21, 110–129. http://dx.doi.org/10.1080/13854040600744839CrossRefGoogle Scholar
Beauregard, M., & Levesque, J. (2006). Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder. Applied Psychophysiology and Biofeedback, 31, 3–20. http://dx.doi.org/10.1007/s10484-006-9001-yCrossRefGoogle ScholarPubMed
Boyle, P. A., Wilson, R. S., Schneider, J. A., Bienias, J. L., & Bennett, D. A. (2008). Processing resources reduce the effect of Alzheimer pathology on other cognitive systems. Neurology, 70, 1534–1542. http://dx.doi.org/10.1212/01.wnl.0000304345.14212.38CrossRefGoogle ScholarPubMed
Buxton, R., Wong, E., & Frank, L. (1998). Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magnetic Resonance in Medicine, 39, 855–864. http://dx.doi.org/10.1002/mrm.1910390602CrossRefGoogle ScholarPubMed
Dias, Á. M. (2010). Supplementary data. Retrieved from http://smt.oda.mat.br/supplementarymaterial_TOPICSGoogle Scholar
Dias, Á. M., & Deusen, A. V. (2010a, September). A case study of brain electrical changes resultant from brain metabolic intervention: HEG neurotherapy with EEG as an independent measure of change. Paper presented at the 1st Annual Joint Meeting of the EEG and Clinical Neuroscience Society. Istanbul, Turkey.Google Scholar
Dias, Á. M., & Deusen, A. V. (2010b, September). HeG neurofeedback in the treatment of Alzheimer s disease: Case study. Paper presented at the 24th European Health Psychology Conference. Cluj-Napoca, Romania.Google Scholar
Dias, Á. M., & Deusen, A. V. (2010c, February). Intentional blood flow perfusion in the prefrontal cortex, cognitive performance and aging. Paper presented at the 38th Annual Meeting International Neuropsychological Society. Acapulco, Mexico.Google Scholar
Dias, Á. M., Deusen, A. V. (2011). A new neurofeedback protocol for depression. The Spanish Journal of Psychology, 14, 374–384. http://dx.doi.org/10.5209/rev_SJOP.2011.v14.n1.34CrossRefGoogle ScholarPubMed
Dias, Á. M., Oda, E., Akiba, H., Arruda, L., & Bruder, L. (2009). Is cognitive dissonance an intrinsic property of the human mind? An experimental solution to a half-century debate. International Journal of Cognitive, Behavioral, Educational and Psychological Studies, 1, 104–108.Google Scholar
Fox, D. J., Tharp, D. F., & Fox, L. C. (2005). Neurofeedback: An alternative and efficacious treatment for Attention Deficit Hyperactivity Disorder. Applied Psychophysiology and Biofeedback, 30, 365–373. http://dx.doi.org/10.1007/s10484-005-8422-3CrossRefGoogle ScholarPubMed
Gersten, A., Perle, J., Raz, A., & Fried, R. (2009). Probing brain oxygenation with near infrared spectroscopy. NeuroQuantology, 7, 258–266.CrossRefGoogle Scholar
Gevensleben, H., Holl, B., Albrecht, B., Vogel, C., Schlamp, D., Kratz, O., … Heinrich, H. (2009). Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. Journal of Child Psychology and Psychiatry, 50, 780–789. http://dx.doi.org/10.1111/j.1469-7610.2008.02033.xCrossRefGoogle ScholarPubMed
Gunderman, R. B., & Bachman, D. M. (2008). Aging and cognitive reserve. Journal of the American College of Radiology, 5, 670–672. http://dx.doi.org/10.1016/j.jacr.2007.12.013CrossRefGoogle ScholarPubMed
Hanyu, H., Sato, T., Shimizu, S., Kanetaka, H., Iwamoto, T., & Koizumi, K. (2008). The effect of education on rCBF changes in Alzheimer's disease: A longitudinal SPECT study. European Journal of uclear Medicine and Molecular Imaging, 35, 2182–2190. http://dx.doi.org/10.1007/s00259-008-0848-4CrossRefGoogle ScholarPubMed
Hosoda, C., Nariai, T., Ishiwata, K., Ishii, K., Matsushima, Y., & Ohno, K. (2010). Correlation between focal brain metabolism and higher brain function in patients with Moyamoya disease. International Journal of Stroke, 5, 367–373. http://dx.doi.org/10.1111/j.1747-4949.2010.00461.xCrossRefGoogle ScholarPubMed
Kalpouzos, G., Eustache, F., & Desgranges, B. (2008). Cognitive reserve and neural networks in normal aging and Alzheimer's disease. Psychology and europsychiatry, 6, 97–105.Google ScholarPubMed
Kandel, E., Schwartz, J. H., & Jessell, T. M. (2000). Principles of euroscience. New York, NY: McGraw-Hill.Google Scholar
Kemppainen, N. M., Aalto, S., Karrasch, M., Någren, K., Savisto, N., Oikonen, V., … Rinne, J. O. (2008). Cognitive reserve hypothesis: Pittsburgh Compound B and fluorodeoxyglucose positron emission tomography in relation to education in mild Alzheimer's disease. Annals of eurology, 63, 112–118. http://dx.doi.org/10.1002/ana.21212CrossRefGoogle ScholarPubMed
Larson, G. E., Haier, R. J., LaCasse, L., & Hazen, K. (1995). Evaluation of a “mental effort” hypothesis for correlations between cortical metabolism and intelligence. Intelligence, 21, 267–278. http://dx.doi.org/10.1016/0160-2896(95)90017-9CrossRefGoogle Scholar
Loo, S., Teale, P., & Reite, M. (1999). EEG correlates of methylphenidate response among children with ADHD: A preliminary report. Biological Psychiatry, 45, 1657–1660. http://dx.doi.org/10.1016/S0006-3223(98)00250-9CrossRefGoogle ScholarPubMed
Roe, C. M., Xiong, C., Miller, J. P., Cairns, N. J., & Morris, J. C. (2008). Interaction of neuritic plaques and education predicts dementia. Journal of the Alzheimer Disorder Association, 22, 188–193. http://dx.doi.org/10.1097/WAD.0b013e3181610fffCrossRefGoogle ScholarPubMed
Staff, R. T., Murray, A. D., Deary, I. J., & Whalley, L. J. (2004). What provides cerebral reserve? Brain, 127, 1191–1199. http://dx.doi.org/10.1093/brain/awh144CrossRefGoogle ScholarPubMed
Starr, J. M., & Lonie, J. (2008). Estimated pre-morbid IQ effects on cognitive and functional outcomes in Alzheimer disease: A longitudinal study in a treated cohort. BMC Psychiatry, 8, 27. http://dx.doi.org/10.1186/1471-244X-8-27CrossRefGoogle Scholar
Stern, Y., Zarahn, E., Habeck, C., Holtzer, R., Rakitin, B. C., Kumar, A., … Brown, T. (2008). A common neural network for cognitive reserve in verbal and object working memory in young but not old. Cerebral Cortex, 18, 959–967. http://dx.doi.org/10.1093/cercor/bhm134CrossRefGoogle Scholar
Tinius, T. (2005). Introduction: Blood flow hemoencephalography. Journal of eurotherapy, 8(3), 1–3. http://dx.doi.org/10.1300/J184v08n03_01CrossRefGoogle Scholar
Valenzuela, M. J. (2008). Brain reserve and the prevention of dementia. Current Opinion in Psychiatry, 21, 296–302. http://dx.doi.org/10.1097/YCO.0b013e3282f97b1fCrossRefGoogle ScholarPubMed
Vance, D. E., Webb, N. M., Marceaux, J. C., Viamonte, S. M., Foote, A. W., & Ball, K. K. (2008). Mental stimulation, neural plasticity, and aging: directions for nursing research and practice. Journal of euroscience ursing, 40, 241–249. http://dx.doi.org/10.1097/01376517-200808000-00008Google ScholarPubMed