Abstract
Mild cognitive impairment (MCI) is a syndrome characterized by a decrease in cognitive abilities, while daily function is maintained. This condition, which is associated with an increased risk for the development of Alzheimer’s disease, has no known definitive treatment at present. In this open-label pilot study we explored the possible benefits of neurofeedback for subjects with MCI. Eleven participants diagnosed with MCI were trained to increase the power of their individual upper alpha band of the electroencephalogram (EEG) signal over the central parietal region. This was achieved using an EEG-based neurofeedback training protocol. Training comprised ten 30-min sessions delivered over 5 weeks. Cognitive and electroencephalographic assessments were conducted before and after training and at 30 days following the last training session. A dose-dependent increase in peak alpha frequency was observed throughout the period of training. Memory performance also improved significantly following training, and this improvement was maintained at 30-day follow-up, while peak alpha frequency returned to baseline at this evaluation. Our findings suggest that neurofeedback may improve memory performance in subjects with mild cognitive impairment, and this benefit may be maintained beyond the training period.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- MCI:
-
Mild cognitive impairment
- AD:
-
Alzheimer’s disease
- EEG:
-
Electroencephalography
- PAF:
-
Peak alpha frequency
- IAF:
-
Individual alpha frequency
- FU:
-
Follow up
- CNS:
-
Central nervous system
References
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders, 5th Edition. Philadelphia, American Psychiatric Association. https://doi.org/10.1176/appi.books.9780890425596.
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. The Clinical Neuropsychologist, 21(1), 110–129. https://doi.org/10.1080/13854040600744839.
Babiloni, C., Frisoni, G. B., Pievani, M., Vecchio, F., Lizio, R., Buttiglione, M., & Rossini, P. M. (2009). Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease. NeuroImage, 44(1), 123–135. https://doi.org/10.1016/j.neuroimage.2008.08.005.
Babiloni, C., Vecchio, F., Lizio, R., Ferri, R., Rodriguez, G., Marzano, N., & Rossini, P. M. (2011). Resting state cortical rhythms in mild cognitive impairment and Alzheimer’s disease: Electroencephalographic evidence. Journal of Alzheimer’s Disease: JAD, 26(Suppl 3), 201–214. https://doi.org/10.3233/JAD-2011-0051.
Becerra, J., Fernández, T., Roca-Stappung, M., Díaz-Comas, L., Galán, L., Bosch, J., & Harmony, T. (2012). Neurofeedback in healthy elderly human subjects with electroencephalographic risk for cognitive disorder. Journal of Alzheimer’s Disease: JAD, 28(2), 357–367. https://doi.org/10.3233/JAD-2011-111055.
Berman, M. H., & Frederick, J. A. (2009). Efficacy of neurofeedback for executive and memory function in dementia. Alzheimer’s & Dementia, 5(4), e8. https://doi.org/10.1016/j.jalz.2009.07.046.
Bink, M., Bongers, I. L., Popma, A., Janssen, T. W. P., & van Nieuwenhuizen, C. (2016). 1-year follow-up of neurofeedback treatment in adolescents with attention-deficit hyperactivity disorder: Randomised controlled trial. British Journal of Psychiatry Open, 2(2), 107–115. https://doi.org/10.1192/bjpo.bp.115.000166.
Boyle, P. A., Wilson, R. S., Aggarwal, N. T., Tang, Y., & Bennett, D. A. (2006). Mild cognitive impairment: Risk of Alzheimer disease and rate of cognitive decline. Neurology, 67(3), 441–445. https://doi.org/10.1212/01.wnl.0000228244.10416.20.
Brodaty, H., Connors, M. H., Ames, D., & Woodward, M. (2014). Progression from mild cognitive impairment to dementia: A 3-year longitudinal study. The Australian and New Zealand Journal of Psychiatry. https://doi.org/10.1177/0004867414536237.
Cummins, T. D. R., Broughton, M., & Finnigan, S. (2008). Theta oscillations are affected by amnestic mild cognitive impairment and cognitive load. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 70(1), 75–81. https://doi.org/10.1016/j.ijpsycho.2008.06.002.
Drago, V., Babiloni, C., Bartrés-Faz, D., Caroli, A., Bosch, B., Hensch, T., & Frisoni, G. B. (2011). Disease tracking markers for Alzheimer’s disease at the prodromal (MCI) stage. Journal of Alzheimer’s Disease: JAD, 26(Suppl 3), 159–199. https://doi.org/10.3233/JAD-2011-0043.
Dwolatzky, T., Whitehead, V., Doniger, G. M., Simon, E. S., Schweiger, A., Jaffe, D., & Chertkow, H. (2004). Validity of the Mindstreams computerized cognitive battery for mild cognitive impairment. Journal of Molecular Neuroscience: MN, 24(1), 33–44. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15314247.
Egner, T., & Sterman, M. B. (2006). Neurofeedback treatment of epilepsy: from basic rationale to practical application. Expert Review of Neurotherapeutics, 6, 247–257. https://doi.org/10.1586/14737175.6.2.247.
Escolano, C., Aguilar, M., & Minguez, J. (2011). EEG-based upper alpha neurofeedback training improves working memory performance. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2011, 2327–2330. https://doi.org/10.1109/IEMBS.2011.6090651.
Ghaziri, J., Tucholka, A., Larue, V., Blanchette-Sylvestre, M., Reyburn, G., Gilbert, G., & Beauregard, M. (2013). Neurofeedback training induces changes in white and gray matter. Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS), 44(4), 265–272. https://doi.org/10.1177/1550059413476031.
Gilbert, J. G., & Levee, R. F. (1971). Patterns of declining memory. Journal of Gerontology, 26(1), 70–75. https://doi.org/10.1093/geronj/26.1.70.
Goldman, R. I., Stern, J. M., Engel, J., & Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport, 13(18), 2487–2492. https://doi.org/10.1097/01.wnr.0000047685.08940.d0.
Hallett, P. E. (1978). Primary and secondary saccades to goals defined by instructions. Vision Research, 18(10), 1279–1296. https://doi.org/10.1016/0042-6989(78)90218-3.
Huang, C., Wahlund, L.-O., Dierks, T., Julin, P., Winblad, B., & Jelic, V. (2000). Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent EEG sources: A cross-sectional and longitudinal study. Clinical Neurophysiology, 111(11), 1961–1967. https://doi.org/10.1016/S1388-2457(00)00454-5.
Iaccarino, L., Chiotis, K., Alongi, P., Almkvist, O., Wall, A., Cerami, C., & Perani, D. (2017). A cross-validation of FDG- and amyloid-PET biomarkers in mild cognitive impairment for the risk prediction to dementia due to Alzheimer’s Disease in a clinical setting. Journal of Alzheimer’s Disease, 59(2), 603–614. https://doi.org/10.3233/JAD-170158.
Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., & Kokmen, E. (1999). Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology, 52(7), 1397–1397. https://doi.org/10.1212/WNL.52.7.1397.
Jack, C. R., Shiung, M. M., Weigand, S. D., O’Brien, P. C., Gunter, J. L., Boeve, B. F., & Petersen, R. C. (2005). Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology, 65(8), 1227–1231. https://doi.org/10.1212/01.wnl.0000180958.22678.91.
Jelic, V., Johansson, S.-E., Almkvist, O., Shigeta, M., Julin, P., Nordberg, A., & Wahlund, L.-O. (2000). Quantitative electroencephalography in mild cognitive impairment: Longitudinal changes and possible prediction of Alzheimer’s disease. Neurobiology of Aging, 21(4), 533–540. https://doi.org/10.1016/S0197-4580(00)00153-6.
Karakaya, T., Fußer, F., Schroder, J., & Pantel, J. (2013). Pharmacological treatment of mild cognitive impairment as a prodromal syndrome of Alzheimer’s disease. Current Neuropharmacology, 11(1), 102–108. https://doi.org/10.2174/157015913804999487.
Karas, G. B., Scheltens, P., Rombouts, S. A. R. B., Visser, P. J., van Schijndel, R. A., Fox, N. C., & Barkhof, F. (2004). Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. NeuroImage, 23(2), 708–716. https://doi.org/10.1016/j.neuroimage.2004.07.006.
Katzir, M., Eyal, T., Meiran, N., & Kessler, Y. (2010). Imagined positive emotions and inhibitory control: The differentiated effect of pride versus happiness. Journal of Experimental Psychology. Learning, Memory, and Cognition, 36(5), 1314–1320. https://doi.org/10.1037/a0020120.
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195. https://doi.org/10.1016/S0165-0173(98)00056-3.
Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, B. J. L., Buitelaar, J. K., & van Schie, H. T. (2009). Long-term effects of neurofeedback treatment in autism. Research in Autism Spectrum Disorders, 3(2), 496–501. https://doi.org/10.1016/j.rasd.2008.10.003.
LaVaque, T. J., Hammond, D. C., Trudeau, D., Monastra, V. J., Perry, J., Lehrer, P., et al. (2002). Template for developing guidelines for the evaluation of the clinical efficacy of psychophysiological interventions. Applied Psychophysiology and Biofeedback, 27, 273–281. https://doi.org/10.1023/A:1021061318355.
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314–324. https://doi.org/10.3758/s13428-011-0168-7.
Mirsky, J. B., Heuer, H. W., Jafari, A., Kramer, J. H., Schenk, A. K., Viskontas, I. V., & Boxer, A. L. (2011). Anti-saccade performance predicts executive function and brain structure in normal elders. Cognitive and Behavioral Neurology: Official Journal of the Society for Behavioral and Cognitive Neurology, 24(2), 50–58. https://doi.org/10.1097/WNN.0b013e318223f6c6.
Miyake, a, Friedman, N. P., Emerson, M. J., Witzki, a H., Howerter, a, & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734.
Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., & Villringer, A. (2003). Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. NeuroImage, 20(1), 145–158. https://doi.org/10.1016/S1053-8119(03)00344-6.
Moretti, D. V., Paternicò, D., Binetti, G., Zanetti, O., & Frisoni, G. B. (2012a). EEG markers are associated to gray matter changes in thalamus and basal ganglia in subjects with mild cognitive impairment. NeuroImage, 60(1), 489–496. https://doi.org/10.1016/j.neuroimage.2011.11.086.
Moretti, D. V., Prestia, A, Fracassi, C., Binetti, G., Zanetti, O., & Frisoni, G. B. (2012b). Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer’s disease. International Journal of Alzheimer’s Disease. https://doi.org/10.1155/2012/253153.
Moretti, D. V., Prestia, A, Fracassi, C., Geroldi, C., Binetti, G., Rossini, P. M., & Frisoni, G. B. (2011). Volumetric differences in mapped hippocampal regions correlate with increase of high alpha rhythm in Alzheimer’s disease. International Journal of Alzheimer’s Disease. https://doi.org/10.4061/2011/208218.
Mufson, E. J., Binder, L., Counts, S. E., DeKosky, S. T., de Toledo-Morrell, L., Ginsberg, S. D., & Scheff, S. W. (2012). Mild cognitive impairment: Pathology and mechanisms. Acta Neuropathologica, 123(1), 13–30. https://doi.org/10.1007/s00401-011-0884-1.
Naveh-Benjamin, M., Guez, J., & Shulman, S. (2004). Older adults’ associative deficit in episodic memory: Assessing the role of decline in attentional resources. Psychonomic Bulletin & Review, 11(6), 1067–1073. https://doi.org/10.3758/BF03196738.
Peltsch, A., Hemraj, A., Garcia, A., & Munoz, D. P. (2011). Age-related trends in saccade characteristics among the elderly. Neurobiology of Aging, 32(4), 669–679. https://doi.org/10.1016/j.neurobiolaging.2009.04.001.
Petersen, R. C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014). Mild cognitive impairment: A concept in evolution. Journal of Internal Medicine, 275(3), 214–228. https://doi.org/10.1111/joim.12190.
Schreckenberger, M., Lange-Asschenfeldt, C., Lange-Asschenfeld, C., Lochmann, M., Mann, K., Siessmeier, T., & Gründer, G. (2004). The thalamus as the generator and modulator of EEG alpha rhythm: A combined PET/EEG study with lorazepam challenge in humans. NeuroImage, 22(2), 637–644. https://doi.org/10.1016/j.neuroimage.2004.01.047.
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., & Sulzer, J. (2017). Closed-loop brain training: The science of neurofeedback. Nature Reviews Neuroscience, 18(2), 86–100. https://doi.org/10.1038/nrn.2016.164.
Strehl, U., Leins, U., Goth, G., Klinger, C., Hinterberger, T., & Birbaumer, N. (2006). Self-regulation of slow cortical potentials: A new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics, 118, e1530–e1540. https://doi.org/10.1111/j.1469-7610.2008.02033.x.
Surmeli, T., Eralp, E., Mustafazade, I., Kos, H., Özer, G. E., & Surmeli, O. H. (2015). Quantitative EEG neurometric analysis—guided neurofeedback treatment in dementia 20 cases. How neurometric analysis is important for the treatment of dementia and as a biomarker? Clinical EEG and Neuroscience. https://doi.org/10.1177/1550059415590750.
Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., & Jack, C. R. (2009). MRI and CSF biomarkers in normal, MCI, and AD subjects: Diagnostic discrimination and cognitive correlations. Neurology, 73(4), 287–293. https://doi.org/10.1212/WNL.0b013e3181af79e5.
Wang, J.-R., & Hsieh, S. (2013). Neurofeedback training improves attention and working memory performance. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 124(12), 2406–2420. https://doi.org/10.1016/j.clinph.2013.05.020.
Wolf, H., Hensel, A., Kruggel, F., Riedel-Heller, S. G., Arendt, T., Wahlund, L.-O., & Gertz, H.-J. (2004). Structural correlates of mild cognitive impairment. Neurobiology of Aging, 25(7), 913–924. https://doi.org/10.1016/j.neurobiolaging.2003.08.006.
Yucha, C., & Gilbert, C. (2004). Evidence-based practice in biofeedback and neurofeedback. Retrieved May 4, 2016, from https://www.aapb.org/files/public/Yucha-Gilbert_EvidenceBased2004.pdf.
Zoefel, B., Huster, R. J., & Herrmann, C. S. (2011). Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. NeuroImage, 54(2), 1427–1431. https://doi.org/10.1016/j.neuroimage.2010.08.078.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Rights and permissions
About this article
Cite this article
Lavy, Y., Dwolatzky, T., Kaplan, Z. et al. Neurofeedback Improves Memory and Peak Alpha Frequency in Individuals with Mild Cognitive Impairment. Appl Psychophysiol Biofeedback 44, 41–49 (2019). https://doi.org/10.1007/s10484-018-9418-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10484-018-9418-0