The Changes of the Infra-Slow EEG Fluctuations of the Brain Potentials under Influence of Infra-Low Frequency Neurofeedback

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This study presents a comparison of the effect on EEG electrical activity in the range of infraslow frequencies of two methods: infra-low frequency EEG biofeedback and heart rate variability training. The study involved 17 healthy subjects aged 21 to 50 years with minor symptoms of a physiological or psychological nature, who did not have a history of neurological or psychiatric diseases. To evaluate the results of the training, we analyzed the spectral power of slow EEG oscillations during the performance of the attention test (Visual Go/NoGo), recorded before and after twenty sessions of biofeedback. Both the subjective assessment of the physiological and psychological state and the results of the visual test showed more pronounced positive changes under the influence of EEG biofeedback compared to the cases of heart rate variability training. A significant increase in the amplitudes of oscillations in the infraslow EEG range was observed only after EEG biofeedback.

作者简介

V. Grin-Yatsenko

Bechtereva Institute of the Human Brain of Russian Academy of Sciences

编辑信件的主要联系方式.
Email: veragrin.ihb@gmail.com
Russia, St. Petersburg

V. Ponomarev

Bechtereva Institute of the Human Brain of Russian Academy of Sciences

Email: veragrin.ihb@gmail.com
Russia, St. Petersburg

J. Kropotov

Bechtereva Institute of the Human Brain of Russian Academy of Sciences

Email: veragrin.ihb@gmail.com
Russia, St. Petersburg

参考

  1. Kropotov JD (2022) The enigma of infra-slow fluctuations in the human EEG. Front Hum Neurosci 16: 928410. https://doi.org/10.3389/fnhum.2022.928410
  2. Аладжалова HA (1956) Сверхмедленные ритмические изменения электрического потенциала головного мозга. Биофизика 1956 (2): 127–136. [Aladjalova NA (1956) Infra-slow rhythmic changes of the brain electrical potential. Biophysica 1: 127–136. (In Russ)].
  3. Аладжалова HA (1962) Медленные электрические процессы в головном мозге. М. Изд-во АН СССР. [Aladjalova NA (1962) Slow electrical processes in the brain. M. Publ House Acad SciUSSR. (In Russ)].
  4. Аладжалова HA (1979) Психофизиологические аспекты сверхмедленной ритмической активности головного мозга. М. Наука. [Aladjalova NA (1979) Psychophysiological Aspects of Brain Infra-Slow Rhythmical Activity. M. Nauka. (In Russ)].
  5. De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM (2006) fMRI resting-state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 29: 1359–1367. https://doi.org/10.1016/j.neuroimage.2005.08.035
  6. Vanhatalo S, Palva JM, Holmes MD, Miller JW, Voipio J, Kaila K (2004) Infraslow oscillations modulate excitability and interictal epileptic activity in the human cortex during sleep. Proc Natl Acad Sci U S A 101: 5053–5057. https://doi.org/10.1073/pnas.0305375101
  7. Van Putten MJAM, Tjepkema-Cloostermans MC, Hofmeijer J (2015) Infraslow EEG activity modulates cortical excitability in postanoxic encephalopathy. J Neurophysiol 113: 3256–3267. https://doi.org/10.1152/jn.00714.2014
  8. Илюхина ВА (1977) Медленные биоэлектрические процессы головного мозга человека. Ленинград. Наука. [Ilyukhina VA (1977) Slow Bioelectrical Processes of the Brain. Leningrad. Science. (In Russ)].
  9. Илюхина ВА (1986) Нейрофизиология функциональных состояний человека. Ленинград Наука. [Ilyukhina VA (1986) Neurophysiology of human functional states. Leningrad. Science. (In Russ)].
  10. Nikulin VV, Fedele T, Mehnert J, Lipp A, Noack C, Steinbrink J, Curio G (2014) Monochromatic ultra-slow (~0.1 Hz) oscillations in the human electroencephalogram and their relation to hemodynamics. Neuroimage 97: 71–80. https://doi.org/10.1016/j.neuroimage.2014.04.008
  11. Rayshubskiy A, Wojtasiewicz TJ, Mikell CB, Bouchard MB, Timerman D, Youngerman BE, McGovern RA, Otten ML, Canoll P, McKhann GM, Hillman EMC (2014). Direct, intraoperative observation of ~0.1Hz hemodynamic oscillations in awake human cortex: Implications for fMRI. Neuroimage 87: 323–331. https://doi.org/10.1016/j.neuroimage.2013.10.044
  12. Egorova N, Veldsman M, Cumming T, Brodtmann A (2017) Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression. Neuroimage Clin 16: 116–124. https://doi.org/10.1016/j.nicl.2017.07.014
  13. Palva JM, Palva S (2012) Infra-slow fluctuations in electrophysiological recordings, blood-oxygenation-level-dependent signals, and psychophysical time series. Neuroimage 62: 2201– 2211. https://doi.org/10.1016/j.neuroimage.2012.02.060
  14. Pfurtscheller G, Schwerdtfeger A, Seither-Preisler A, Brunner C, Aigner CS, Brito J, Carmo MP, Andrade A (2017) Brain–heart communication: Evidence for “central pacemaker” oscillations with a dominant frequency at 0.1 Hz in the cingulum. Clin Neurophysiol 128: 183–193. https://doi.org/10.1016/j.clinph.2016.10.097
  15. Noordmans HJ, van Blooijs D, Siero JCW, Zwanenburg JJM, Klaessens JHGM, Ramsey NF (2018) Detailed view on slow sinusoidal, hemodynamic oscillations on the human brain cortex by Fourier transforming oxy/deoxy hyperspectral images. Hum Brain Mapp 39: 3558–3573. https://doi.org/10.1002/hbm.24194
  16. Lorincz ML, Kekesi KA, Juhasz G, Crunelli V, Hughes SW (2009) Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm. Neuron 63: 683–696. https://doi.org/10.1016/j.neuron.2009.08.012
  17. Low MD, Swift SJ (1971) The contingent negative variation and the “resting” D.C. potential of the human brain: effects of situational anxiety. Neuropsychologia 9: 203–208. https://doi.org/10.1016/0028-3932(71)90044-3
  18. Broyd SJ, Helps SK, Sonuga-Barke EJ (2011) Attention-induced deactivations in very low-frequency EEG oscillations: differential localization according to ADHD symptom status. PLoS One 6: e17325. https://doi.org/10.1371/journal.pone.0017325
  19. Meda SA, Wang Z, Ivleva EI, Poudyal G, Keshavan MS, Tamminga CA, Sweeney JA, Clementz BA, Schretlen DJ, Calhoun VD, Lui S, Damaraju E, Pearlson GD (2015) Frequency-Specific Neural Signatures of Spontaneous Low-Frequency Resting-State Fluctuations in Psychosis: Evidence From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium. Schizophr Bull 41: 1336–1348. https://doi.org/10.1093/schbul/sbv064
  20. Guo X, Chen H, Long Z, Duan X, Zhang Y, Chen H (2017) Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder. Sci Rep 7: 39822. https://doi.org/10.1038/srep39822
  21. Reid PD, Daniels B, Rybak M, Turnier-Shea Y, Pridmore S (2002) Cortical excitability of psychiatric disorders: reduced post-exercise facilitation in depression compared to schizophrenia and controls. Aust N Z J Psychiatry 36: 669–673. https://doi.org/10.1046/j.1440-1614.2002.01082.x
  22. Badawy RA, Loetscher T, Macdonell RA, Brodtmann A (2012) Cortical excitability and neurology: insights into the pathophysiology. Funct Neurol 27: 131–145.
  23. Tommasin S, Mascali D, Gili T, Eid Assan I, Moraschi M, Fratini M, Wise RG, Macaluso E, Mangia S, Giove F (2017) Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network. Front Phys 5: 31. https://doi.org/10.3389/fphy.2017.00031
  24. Othmer S, Othmer SF, Legarda S (2011) Clinical neurofeedback: Training brain behavior. Treatment strategies. Pediatr Neurol Psych 2: 67–73.
  25. Othmer S, Othmer SF (2009). Post-traumatic stress disorder – the neurofeedback remedy. Biofeedback 37(1): 24–31. https://doi.org/10.5298/1081-5937-37.1.24
  26. Othmer S, Othmer SF (2016) Infra-low frequency neurofeedback for optimum performance. Biofeedback 44(2): 81–89. https://doi.org/10.5298/1081-5937-44.2.07
  27. Legarda SB, McMahon D, Othmer S, Othmer S (2011) Clinical neurofeedback: Case studies, proposed mechanism, and implications for pediatric neurology practice. J Child Neurol 26(8): 1045–1051. https://doi.org/10.1177/0883073811405052
  28. Grin-Yatsenko VA, Othmer S, Ponomarev VA, Evdokimov SA, Konoplev YY, Kropotov JD (2018) Infra-low frequency neurofeedback in depression: Three case studies. Neuroregulation 5: 30–42. https://doi.org/10.15540/nr.5.1.30
  29. Grin-Yatsenko VA, Kropotov JD (2020) Effect of Infra-Low Frequency (ILF) Neurofeedback on the Functional State of the Brain in Healthy and Depressed Individuals. In: HW Kirk (ed) Restoring the Brain Neurofeedback as an Integrative Approach to Health. https://doi.org/10.4324/9780429275760
  30. Grin-Yatsenko VA, Kara O, Evdokimov SA, Gregory M, Othmer S, Kropotov JD (2020) Infra-Low Frequency Neuro Feedback Modulates Infra-Slow Oscillations of Brain Potentials: A Controlled Study. J Biomed Eng 4: 1–11. https://doi.org/10.17303/jber.2020.4.104
  31. Dobrushina OR, Vlasova RM, Rumshiskaya AD, Litvinova LD, Mershina EA, Sinitsyn VE, Pechenkova EV (2020) Modulation of Intrinsic Brain Connectivity by Implicit Electroencephalographic Neurofeedback. Front Hum Neurosci 14(192): 1–13. https://doi.org/10.3389/fnhum.2020.00192
  32. Othmer SF (2017) The Protocol guide for neurofeedback clinicians. 6th ed. Los Angeles, CA. EEG Info.
  33. Kropotov J (2009) Quantitative EEG, event-related potentials, and Neurotherapy. San Diego, CA. Acad Press/Elsevier. https://doi.org/10.1016/B978-0-12-374512-5.X0001-1
  34. Vigario RN (1997) Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalogr Clin Neurophysiol 103: 395–404. https://doi.org/10.1016/S0013-4694(97)00042-8
  35. Jung TP, Makeig S, Westerfeld M, Townsend J, Courchesne E, Sejnowski TJ (2000) Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin Neurophysiol 111: 1745–1758. https://doi.org/10.1016/S1388-2457(00)00386-2
  36. Perrin F, Pernier J, Bertrand O, Echallier JF (1989) Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol 72: 184–187. https://doi.org/10.1016/0013-4694(89)90180-6
  37. Perrin F, Pernier J, Bertrand O, Echallier JF (1990) Spherical splines for scalp potential and current density mapping. Corrigenda EEG 02274. Electroencephalogr Clin Neurophysiol 76: 565. https://doi.org/10.1016/0013-4694(89)90180-6
  38. Kayser J, Tenke CE (2006) Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin Neurophysiol 117: 348–368. https://doi.org/10.1016/j.clinph.2005.08.034
  39. Kayser J, Tenke CE (2006) Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clin Neurophysiol 117: 369–380. https://doi.org/10.1016/j.clinph.2005.08.033
  40. Maris E, Oostenveld R (2007) Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164(1): 177–190. https://doi.org/10.1016/j.jneumeth.2007.03.024
  41. Oostenveld R, Fries P, Maris E, Schoffelen JM (2011) FieldTrip: Open-source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 156869. https://doi.org/10.1155/2011/156869
  42. Helps S, James C, Debener S, Karl A, Sonuga-Barke EJ (2008) Very low frequency EEG oscillations and the resting brain in young adults: a preliminary study of localisation, stability and association with symptoms of inattention. J Neural Transm (Vienna) 115(2): 279–285. https://doi.org/10.1007/s00702-007-0825-2

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