ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Nuidel I. V., Kolosov A. V., Permiakov S. A., Egorov I. S., Polevaia S. A., Yakhno V. G. Mathematical model for controlling brain neuroplasticity during neurofeedback. Izvestiya VUZ. Applied Nonlinear Dynamics, 2024, vol. 32, iss. 4, pp. 472-491. DOI: 10.18500/0869-6632-003109, EDN: CAAKSH

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Language: 
Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003109
EDN: 
CAAKSH

Mathematical model for controlling brain neuroplasticity during neurofeedback

Autors: 
Nuidel Irina Vladimirovna, Institute of Applied Physics of the Russian Academy of Sciences
Kolosov Alexey Vadimovich, Institute of Applied Physics of the Russian Academy of Sciences
Permiakov Sergei Alexandrovich, Lobachevsky State University of Nizhny Novgorod
Egorov Igor S, Lobachevsky State University of Nizhny Novgorod
Polevaia Sofia Alexandrovna, Lobachevsky State University of Nizhny Novgorod
Yakhno Vladimir Grigorevich, Institute of Applied Physics of the Russian Academy of Sciences
Abstract: 

The purpose of this work is to apply a model of interaction between thalamocortical system modules to control brain neuroplasticity.

Methods. Psychophysiological experiments on neurofeedback are being carried out, which consist of light stimulation of the eyes with monofrequency light pulses in the range of 4...20 Hz and recording the bioelectrical activity of the brain. As a characteristic of maturity, brain rhythms use the combination of the presence or absence in the bioelectrical activity of the brain of a dominant peak frequency in the alpha range of the EEG, the effect of assimilation of the rhythms imposed by stimulation, and the presence of a multiplying effect from the rhythms imposed by stimulation. Solutions to the model of an elementary thalamocortical cell, which is described by a system of differential equations, corresponding to a psychophysiological experiment are considered. The model is implemented using the Python.

Results. The model parameters are selected in such a way as to achieve a qualitative correspondence of the spectral characteristics of the obtained solutions with the bioelectrical activity of the subject’s brain. Rhythmic maturity is assessed based on the parameters of the thalamocortical cell model. The brightness and frequency characteristics of light stimuli are selected based on the prediction of the model, the input of which is supplied with various variants of pulse sequences.

Conclusion. A method has been developed for digital diagnostics of the level of brain rhythm maturity based on a comparison of modeling results and data from a psychophysiological experiment on neurofeedback. The evolution of model solutions depending on its parameters simulates the process of biocontrol of brain neuroplasticity, taking into account the initial level of rhythmic maturity and stress-induced distortions of neurodynamics. Experiments on the model with different parameters of the model and external signal can be used in the development of new neurofeedback protocols.

Key words: 
thalamocortical cell model
neurofeedback
neuroplasticity
bioelectrical activity of the brain
assessment of brain rhythm maturity
Acknowledgments: 
The work on conducting psychophysiological experiments and data processing was supported by the Russian Science Foundation, grant No. 22-18-20075, the modeling work was partially supported by the Russian Science Foundation grant No. 22-18-20075 and the Ministry of Science and Higher Education of the Russian Federation in within the framework of the state assignment of the Institute of Applied Physics RAS, project No. FFUF-2021-0014
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Received: 
16.11.2023
Accepted: 
29.01.2024
Available online: 
22.05.2024
Published: 
31.07.2024
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