Russian Journal of PhysiologyRussian Journal of PhysiologyРоссийский физиологический журнал им. И.М. Сеченова0869-81392658-655XThe Russian Academy of Sciences65156610.31857/S0869813923050047XQOVEGEXPERIMENTAL ARTICLESЭКСПЕРИМЕНТАЛЬНЫЕ СТАТЬИThe Changes of the Infra-Slow EEG Fluctuations of the Brain Potentials under Influence of Infra-Low Frequency NeurofeedbackИзменение сверхмедленных колебаний потенциалов мозга под влиянием БОС-тренинга по сверхмедленным частотам ЭЭГGrin-YatsenkoV. А.Гринь-ЯценкоВ. А.veragrin.ihb@gmail.comPonomarevV. А.ПономаревВ. А.veragrin.ihb@gmail.comKropotovJ. D.КропотовЮ. Д.veragrin.ihb@gmail.comBechtereva Institute of the Human Brain of Russian Academy of SciencesИнститут мозга человека им. Н.П. Бехтеревой Российской академии наук01052023109560061101022025Copyright ©; 2023, В.А. Гринь-Яценко, В.А. Пономарев, Ю.Д. КропотовCopyright ©; 2023, В.А. Гринь-Яценко, В.А. Пономарев, Ю.Д. Кропотов2023В.А. Гринь-Яценко, В.А. Пономарев, Ю.Д. КропотовВ.А. Гринь-Яценко, В.А. Пономарев, Ю.Д. Кропотовhttps://journals.eco-vector.com/0869-8139/article/view/651566

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.

В настоящем исследовании представлено сравнение влияния на электрическую активность ЭЭГ в диапазоне сверхмедленных частот двух видов воздействия: ЭЭГ биоуправления по сверхмедленным колебаниям и тренировки вариабельности сердечного ритма. В исследовании приняли участие 17 здоровых испытуемых в возрасте от 21-го до 50-ти лет с незначительно выраженными симптомами физиологического и/или психологического характера, не имевших в анамнезе неврологических и психических заболеваний. Для оценки результатов тренинга проводился анализ спектральной мощности медленных колебаний ЭЭГ во время выполнения теста на внимание (Visual Go/NoGo), зарегистрированных до и после двадцати сеансов биоуправления. Как субъективная оценка физиологического и психологического состояния, так и результаты выполнения зрительного теста показали более выраженные положительные сдвиги под влиянием ЭЭГ биоуправления по сравнению со случаями тренировки вариабельности сердечного ритма. Значительное повышение амплитуд в сверхмедленном диапазоне наблюдалось только после ЭЭГ биоуправления.

neurofeedbackelectroencephalograminfra-slow EEG oscillationsheart rate variabilityЭЭГбиоуправлениеэлектроэнцефалографиясверхмедленные колебания потенциалов мозгавариабельность сердечного ритмаАвторы благодарят за поддержку Bernhard Wandernoth, инженера компании BEE Medic, разработавшего систему ЭЭГ-БОС Cygnet, использованную в данной работе.Kropotov JD (2022) The enigma of infra-slow fluctuations in the human EEG. Front Hum Neurosci 16: 928410. https://doi.org/10.3389/fnhum.2022.928410Аладжалова 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)].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)].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)].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.2005.08.035Vanhatalo 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.0305375101Van 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.00714.2014Илюхина ВА (1977) Медленные биоэлектрические процессы головного мозга человека. Ленинград. Наука. [Ilyukhina VA (1977) Slow Bioelectrical Processes of the Brain. Leningrad. Science. (In Russ)].Илюхина ВА (1986) Нейрофизиология функциональных состояний человека. Ленинград Наука. [Ilyukhina VA (1986) Neurophysiology of human functional states. Leningrad. Science. (In Russ)].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.2014.04.008Rayshubskiy 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.2013.10.044Egorova 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.2017.07.014Palva 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.2012.02.060Pfurtscheller 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.2016.10.097Noordmans 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.24194Lorincz 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.2009.08.012Low 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)90044-3Broyd 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.0017325Meda 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/sbv064Guo 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/srep39822Reid 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.xBadawy RA, Loetscher T, Macdonell RA, Brodtmann A (2012) Cortical excitability and neurology: insights into the pathophysiology. Funct Neurol 27: 131–145.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.2017.00031Othmer S, Othmer SF, Legarda S (2011) Clinical neurofeedback: Training brain behavior. Treatment strategies. Pediatr Neurol Psych 2: 67–73.Othmer S, Othmer SF (2009). Post-traumatic stress disorder – the neurofeedback remedy. Biofeedback 37(1): 24–31. https://doi.org/10.5298/1081-5937-37.1.24Othmer S, Othmer SF (2016) Infra-low frequency neurofeedback for optimum performance. Biofeedback 44(2): 81–89. https://doi.org/10.5298/1081-5937-44.2.07Legarda 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/10.1177/0883073811405052Grin-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.5.1.30Grin-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/10.4324/9780429275760Grin-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.2020.4.104Dobrushina 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.2020.00192Othmer SF (2017) The Protocol guide for neurofeedback clinicians. 6th ed. Los Angeles, CA. EEG Info.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.X0001-1Vigario 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)00042-8Jung 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)00386-2Perrin 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)90180-6Perrin 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)90180-6Kayser 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.2005.08.034Kayser 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.2005.08.033Maris 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.2007.03.024Oostenveld 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/10.1155/2011/156869Helps 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