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Neuroimaging Study of Alpha and Beta EEG Biofeedback Effects on Neural Networks

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Abstract

Neural networks interaction was studied in healthy men (20–35 years old) who underwent 20 sessions of EEG biofeedback training outside the MRI scanner, with concurrent fMRI–EEG scans at the beginning, middle, and end of the course. The study recruited 35 subjects for EEG biofeedback, but only 18 of them were considered as “successful” in self-regulation of target EEG bands during the whole course of training. Results of fMRI analysis during EEG biofeedback are reported only for these “successful” trainees. The experimental group (N = 23 total, N = 13 “successful”) upregulated the power of alpha rhythm, while the control group (N = 12 total, N = 5 “successful”) beta rhythm, with the protocol instructions being as for alpha training in both. The acquisition of the stable skills of alpha self-regulation was followed by the weakening of the irrelevant links between the cerebellum and visuospatial network (VSN), as well as between the VSN, the right executive control network (RECN), and the cuneus. It was also found formation of a stable complex based on the interaction of the precuneus, the cuneus, the VSN, and the high level visuospatial network (HVN), along with the strengthening of the interaction of the anterior salience network (ASN) with the precuneus. In the control group, beta enhancement training was accompanied by weakening of interaction between the precuneus and the default mode network, and a decrease in connectivity between the cuneus and the primary visual network (PVN). The differences between the alpha training group and the control group increased successively during training. Alpha training was characterized by a less pronounced interaction of the network formed by the PVN and the HVN, as well as by an increased interaction of the cerebellum with the precuneus and the RECN. The study demonstrated the differences in the structure and interaction of neural networks involved into alpha and beta generating systems forming and functioning, which should be taken into account during planning neurofeedback interventions. Possibility of using fMRI-guided biofeedback organized according to the described neural networks interaction may advance more accurate targeting specific symptoms during neurotherapy.

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Acknowledgements

This study was supported by Russian Science Foundation (RSF grant N 16-15-00183).

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Authors and Affiliations

  1. Research Institute of Molecular Biology and Biophysics, Novosibirsk, Russia, 630117

    Mark B. Shtark, Lyudmila I. Kozlova, Dmitriy D. Bezmaternykh & Mikhail Ye. Mel’nikov

  2. International Tomography Center, Siberian Division of Russian Academy of Sciences, Novosibirsk, Russia, 630090

    Andrey A. Savelov

  3. Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia, 630090

    Mark B. Shtark, Lyudmila I. Kozlova, Dmitriy D. Bezmaternykh & Mikhail Ye. Mel’nikov

  4. University of South Carolina, School of Medicine-Greenville, Greenville, SC, 29605, USA

    Estate M. Sokhadze

  5. Department of Biomedical Sciences, University of South Carolina, School of Medicine-Greenville, 200 Patewood Dr. #A200, Greenville, SC, 29615, USA

    Estate M. Sokhadze

Authors
  1. Mark B. Shtark
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  2. Lyudmila I. Kozlova
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  3. Dmitriy D. Bezmaternykh
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  4. Mikhail Ye. Mel’nikov
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  5. Andrey A. Savelov
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  6. Estate M. Sokhadze
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Correspondence to Estate M. Sokhadze.

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The authors do not have any potential conflict of interest to disclose. All authors therefore declare that they do not have any conflict of interests.

Ethical Approval

All procedures performed in this study that involved human subjects were in accordance with the ethical standards and in compliance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was approved by the Ethics Committee of the Research Institute of Molecular Biology and Biophysics. All subjects signed informed consent form approved by the Ethics Committee.

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Shtark, M.B., Kozlova, L.I., Bezmaternykh, D.D. et al. Neuroimaging Study of Alpha and Beta EEG Biofeedback Effects on Neural Networks. Appl Psychophysiol Biofeedback 43, 169–178 (2018). https://doi.org/10.1007/s10484-018-9396-2

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  • DOI: https://doi.org/10.1007/s10484-018-9396-2

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