Научная статья на тему 'Current Trends and Prospects for Development of Non-Invasive Brain Stimulation'

Current Trends and Prospects for Development of Non-Invasive Brain Stimulation Текст научной статьи по специальности «Медицинские технологии»

CC BY
43
11
i Надоели баннеры? Вы всегда можете отключить рекламу.
Ключевые слова
non-invasive brain stimulation / closed-loop adaptive neurostimulation / transcranial magnetic and electrical stimulation / acoustic stimulation / audiovisual stimulation / rhythmic processes of the body / automatic modulation

Аннотация научной статьи по медицинским технологиям, автор научной работы — S.A. Polevaya, S.B. Parin, A.I. Fedotchev

This review analyzes current trends in the development of traditional (open-loop) methods of non-invasive brain stimulation, as well as promising directions for the development of closed-loop methods of adaptive neurostimu-lation. The main focus is on studies using non-invasive magnetic and electrical stimulation, as well as acoustic and audiovisual stimulation. The possibilities and prospects for using these technologies as a tool in carrying out a wide range of rehabilitation procedures are analyzed. The results of the authors' own research in this direction are presented.

i Надоели баннеры? Вы всегда можете отключить рекламу.
iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.
i Надоели баннеры? Вы всегда можете отключить рекламу.

Текст научной работы на тему «Current Trends and Prospects for Development of Non-Invasive Brain Stimulation»

CURRENT TRENDS AND PROSPECTS FOR DEVELOPMENT OF NON-INVASIVE BRAIN STIMULATION

S.A. Polevaya1, S.B. Parin1, A.I. Fedotchev2*

1 National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603950, Russia;

2 Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya St., Pushchino, Moscow Region, 142290, Russia.

* Corresponding author: fedotchev@mail.ru

Abstract. This review analyzes current trends in the development of traditional (open-loop) methods of non-invasive brain stimulation, as well as promising directions for the development of closed-loop methods of adaptive neurostimulation. The main focus is on studies using non-invasive magnetic and electrical stimulation, as well as acoustic and audiovisual stimulation. The possibilities and prospects for using these technologies as a tool in carrying out a wide range of rehabilitation procedures are analyzed. The results of the authors' own research in this direction are presented.

Keywords: non-invasive brain stimulation, closed-loop adaptive neurostimulation, transcranial magnetic and electrical stimulation, acoustic stimulation, audiovisual stimulation, rhythmic processes of the body, automatic modulation.

List of Abbreviations

EEG - electroencephalogram

Introduction

One of the most promising and rapidly developing areas of neurophysiology is the creation, improvement and clinical testing of non-invasive methods of brain stimulation, including transcranial magnetic and electrical influences, acoustic and audiovisual stimulation. To date, the range of conditions for the successful use of these methods is extremely wide, as are the specific characteristics of the therapeutic effects used.

It is known that transcranial magnetic stimulation of the brain is widely used in the treatment of neurological and psychiatric diseases (Burke et al.., 2019; Gonsalvez et al, 2021), in cognitive science for long-term modulation of the activity of the stimulated area of the cerebral cortex (Bakulin et al., 2020; Begemann et al., 2020), during cognitive rehabilitation of patients with focal brain lesions (Kalantarova et al., 2020; Draisma et al., 2020; Khrulev et al., 2022), as well as in the treatment of symptoms of post-traumatic stress disorder (Kan et al., 2020). Transcranial electrical stimulation is considered one of the most promising approaches to non-invasive modulation of neuro-

plastic processes in patients with movement disorders (Bakulin et al., 2019; Stolbkov, Gerasimenko, 2021; Popyvanova et al., 2022), to non-drug treatment of depressive disorders (Poydasheva et al., 2020; Piccoli et al., 2022; Hao et al. , 2023) and to the rehabilitation of patients with post-stroke aphasia (Belopasova et al., 2020).

It is also known that specially organized acoustic influences are successfully used to correct psychosomatic disorders (Sekirin & May-brodskaya, 2019), to strengthen mental health (Geiser et al., 2020) and psychological rehabilitation of patients with impaired motor functions (Kotelnikova et al., 2021), as well as to improve sleep and memory (Malkani & Zee, 2020; Wunderlin et al., 2021; Stanier et al., 2022). Audiovisual stimulation has even more pronounced therapeutic potential due to the participation of resonance mechanisms of brain activity, mechanisms of multisensory integration and neuroplasticity (Ashanina & Senik, 2018; Kotov et al., 2020; Roy et al., 2021). Thus, audiovisual influences are successfully used in the treatment of neurodegenerative diseases (Yang et al., 2021), to enhance the adaptive capabilities of the body of athletes (Golovin et al., 2018), to improve the functional state and health of a person (Korolev, Savchenko, 2018;

Sysoev et al., 2018), in the prevention of overwork in students (Pats & Goryunova, 2018), in the restoration of motor and cognitive functions after severe traumatic brain injury (De Luca et al., 2021).

An analysis of the literature shows that two main lines of research can be distinguished in the area under consideration. One of them includes methods of non-invasive brain stimulation based on the traditional approach, in which the stimulation parameters are set in advance and remain unchanged during the course of stimulation. Another line has been formed relatively recently and includes methods of adaptive neurostimulation with feedback from the current physiological parameters of a person. In recent years, the number of publications in both lines of research has been rapidly increasing, which makes it necessary to identify the most promising directions for further development of these lines of non-invasive brain stimulation.

In this regard, the purpose of the presented review is to analyze current trends in the development of traditional methods of non-invasive brain stimulation, as well as promising directions for the development of adaptive neurostimulation with feedback. The main attention is paid to the consideration of studies using non-invasive magnetic and electrical influences, as well as acoustic and audiovisual stimulation. The possibilities and prospects for using these technologies as a tool in carrying out a wide range of rehabilitation procedures are analyzed. The results of the authors' own research in this direction are presented.

Development trends of traditional methods of non-invasive brain stimulation

One of the progressive trends is the use of combined effects. Thus, a combination of meditation or hypnosis procedures with transcranial electrical stimulation of the brain led to an increase in neuroplasticity and an increase in the clinical effectiveness of the combined interventions relative to their isolated effects (Rebello-Sanchez et al., 2022). Transcranial alternating current stimulation combined with acoustic stimulation (40 Hz tone) was found to be a safe and easily tolerated treatment for cognitive

function in patients with Alzheimer's disease, while separate use of electrical and acoustic stimulation was significantly less effective (Liu et al., 2023).

A progressive trend in increasing the effectiveness of transcranial electrical stimulation with direct current is the use of small ring electrodes, which allows increasing the focality of stimulation (Poydasheva et al., 2021). Other authors also point out the importance of spatial resolution and focality of transcranial magnetic stimulation, which allows differential stimulation of cortical areas when correcting cognitive functions (Numssen et al., 2023).

Another trend in the development of traditional methods of non-invasive brain stimulation is the widespread use of functional brain imaging data. For example, it has been shown that rhythmic transcranial magnetic stimulation under the control of an electroencephalogram (Poydasheva et al., 2019) or functional magnetic resonance imaging (Lagoda et al., 2021) is a highly effective treatment for cognitive disorders. It is believed that the strong and long-lasting oscillations caused in the cerebral cortex by rhythmic stimulation may help restore the natural frequencies of neural activity in older people to those characteristic of younger and healthier brains (Qiao et al., 2022). Particularly promising are individualized treatments in which the frequency and location of stimulation are adjusted according to pathological brain conditions identified by functional brain imaging (Chino et al., 2023).

Despite the intensive development and noted research prospects, in general, existing traditional methods of non-invasive brain stimulation are characterized by a number of disadvantages, such as low efficiency, high variability and poor reproducibility of the results obtained (Janssens, Sack, 2021; Antal et al., 2022; Schutter et al., 2023). The reason for the listed shortcomings is the fact that when organizing these therapeutic interventions, empirically specified parameters are used, which remain constant during stimulation and do not depend on changes in the patient's condition. This approach does not take into account the dynamic nature of the endogenous oscillatory activity of

the nervous system. In fact, stimuli are presented during different physiological mi-crostates of the brain, leading to high variability in the effect of individual stimulus and to a weak overall stimulation effect (Bakulin et al., 2021; Kasten & Herrmann, 2022). As a result, untimely applied neurostimulation may be ineffective or cause unwanted side effects (Zanos, 2019; Provenza et al., 2019).

Overcoming these shortcomings is achieved in an intensively developing line of research -adaptive neurostimulation, in which feedback from the current physiological parameters of a person is used when organizing non-invasive brain stimulation procedures.

Trends and prospects for the development of adaptive neurostimulation with feedback

Adaptive neurostimulation methods use sensory influences that adapt to the current parameters of dynamic processes characteristic of a given patient using control feedback signals from various physiological parameters of the body (Lo & Widge, 2017; Oxley & Opie, 2019). The key feature of adaptive neurostimulation methods is that the adjustment of the parameters of the therapeutic effect, controlled by feedback signals from the patient's current physiological indicators, is carried out automatically, without the participation of his consciousness (Zhou & Miller, 2019; Tervo et al., 2022). Compared with traditional brain stimulation methods, adaptive feedback neurostimulation can improve the effectiveness of therapy, eliminate the long initial period for programming and adjusting the stimulator, provide individualized treatment, and automatically maintain adaptive stimulation parameters (Hosain et al., 2014; Prosky et al., 2021).

Thus, the use of feedback from current human physiological parameters provides adaptive neurostimulation methods with a number of advantages. First, feedback signals modulate or adapt therapeutic interventions in response to physiological changes and thus provide more effective and efficient therapy (Sun & Morrell, 2014; Potter et al., 2014). Secondly, thanks to the principle of feedback closure, the current

dynamics of microstates of the nervous system are taken into account (Vosskuhl et al., 2018; Dick & Nozdrachev, 2020; de Bock et al., 2020; Hu et al., 2023). Thirdly, therapeutic stimulation procedures achieve high personalization of effects, corresponding to the most promising directions in the development of methods of non-invasive brain stimulation -brain state-dependent closed-loop stimulation (Bergmann, 2018; Bradley et al., 2022; Far-khondeh et al., 2022) and physiologically informed adaptive neuromodulation (Wendt et al., 2022; Nasr et al., 2022; Weiss et al., 2023).

One of the progressive trends in the development of adaptive neurostimulation methods is the use of feedback signals from the patient's rhythmic processes - rhythms of the cardiovascular and respiratory systems, as well as electroencephalogram (EEG) rhythms. These rhythmic processes are closely interrelated and form the basis of the natural homeostatic regulation of functions; they demonstrate the phenomena of synchronization and resonance and are characterized by high sensitivity to the action of external factors (Fedotchev et al., 2021a). In addition, these rhythmic processes are a source of interoceptive signals, which provide the perception of internal bodily sensations (Quadt et al., 2018; Gentsch et al., 2019; Gibson, 2019). Interoception disorders are currently considered as a potential target for therapeutic intervention in psychosomatic diseases (Khalsa et al., 2018; Dobrushina et al., 2020). An important conceptual basis for this line of non-invasive brain stimulation is also the recently intensively developed ideas about "oscil-lopathies" and the possibilities of "oscillother-apy" (Takeuchi, Berenyi, 2020), according to which external rhythmic influences can direc-tionally modulate endogenous oscillations through resonance mechanisms or rhythm aqui-sition mechanisms. Therefore, oscillations of the neural network can be effectively used as therapeutic targets when organizing "oscillo-therapy" procedures through the use of actively developing methods of adaptive neurostimulation with feedback (Foldi et al., 2021; Takeuchi et al., 2022).

For example, back in 1996 it was shown that rapid relief of pain syndromes and preservation of pain relief effects for a long time is achieved even with a single application of electrical neurostimulation, automatically controlled by the patient's breathing rhythm (Fedotchev, 1996). Subsequently, electrical stimulation controlled by the patient's breathing was successfully used by a number of authors in the treatment of chronic neuropathic pain (Li et al., 2016; Karri et al., 2018, 2021). Complex acoustic interventions automatically controlled by the patient's current heart rate variability have been successfully used to achieve a state of relaxation (Yu et al., 2018).

Adaptive neurostimulation methods that use feedback from the patient's EEG have gained the most popularity and active development. This is due to the advantages of EEG such as ease of use, non-invasiveness, high temporal resolution and the ability to extract data in real time (Koenig et al., 2020; Jangwan et al., 2022). Numerous studies have shown that non-invasive sensory influences, synchronized with certain current EEG parameters, can improve sleep quality, enhance cognitive functions and memory consolidation processes.

For example, increased efficiency has been demonstrated for transcranial magnetic stimulation synchronized with certain phases of EEG oscillations (Stefanou et al., 2019; Ding et al., 2022). In the treatment of pharmacotherapy-re-sistant depressive disorders, even single magnetic influences controlled in real time by the power of the occipital alpha rhythm of the EEG were effective (Zrenner et al., 2020). When using acoustic influences controlled by feedback signals from slow-wave EEG components (Schneider et al.., 2020; Ruch et al., 2022) or sleep EEG spindles (Ngo et al., 2022), the possibility of significant improvements in sleep quality and memory consolidation processes was demonstrated. With audiovisual stimulation, automatically controlled by feedback signals from narrow-frequency spectral components of the EEG, successful elimination of anxiety and depression was observed (Pino, 2021).

Another trend in the development of methods of adaptive neurostimulation with feedback

is the use of computer transformations of the current parameters of bioelectrical activity of the brain into acoustic signals. Thus, the presentation of acoustic stimuli generated in real time by software-controlled transformation of the subject's dominant EEG rhythms into sound stimuli causes a clinically significant reduction in symptoms of post-traumatic stress (Tegeler et al., 2017; Tegeler et al., 2020), and also leads to optimization of autonomic functions and improved sleep quality (Shaltout et al., 2018; Tegeler et al., 2023). The authors argue that realtime updating of one's own EEG patterns and resonance between audible acoustic signals and oscillatory brain networks provide the body with the ability to auto-calibrate, relax, and overcome persistent pathological conditions (Tegeler et al., 2020).

An interesting version of EEG-controlled acoustic stimulation has been successfully used in the bioacoustic correction method, which consists of presenting a person with acoustic signals obtained by computer conversion of the current EEG (Konstantinov et al., 2014, 2015). The method allows one to "hear" the work of the brain in real time and has been successfully used to correct unfavorable functional states with disorders of the cognitive and emotional-volitional sphere (Ivanova & Kormushkina, 2021; Shchegolkov et al., 2022).

Computer conversion of current EEG parameters into therapeutic sensory influences was also used in our studies. Initially, a musical neurointerface was developed in which the current values of the subject's dominant spectral EEG components (EEG oscillators) are converted into music-like signals, timbre reminiscent of the sounds of a flute, smoothly varying in pitch and intensity. This neurointerface has been successfully used in the correction of stress-induced disorders (Fedotchev et al., 2018). Subsequently, the described method of EEG-controlled musical stimulation was improved by adding a second feedback loop, in which, simultaneously with music-like stimulation, photic rhythmic stimuli are presented, formed on the basis of the patient's native EEG (Fedotchev et al., 2019a; Fedotchev et al., 2022). The created method of light and music

stimulation with double feedback from the EEG was successfully used to eliminate the risks of reliability of high-tech specialists (Fedotchev et al., 2019b, 2021b), in the treatment of posttraumatic stress and professional burnout (Fedotchev et al., 2021c), as well as for cognitive rehabilitation of patients with stroke (Mukhina et al., 2021).

Our research also outlined a promising approach to increasing the effectiveness of EEG-controlled sensory stimulation. This approach consists of using resonance scanning, or LED rhythmic photostimulation with a gradually increasing frequency in the range of basic EEG rhythms (Savchuk et al., 2022). It has been experimentally shown that resonance scanning can serve as a kind of preliminary tuning of the brain, causing activation of potential resonators in the EEG spectrum and increasing brain responses to subsequent EEG-controlled adaptive neurostimulation (Fedotchev et al., 2023). Previously, modeling studies proved the possibility of enhancing cognitive activity and improving overall well-being through the interaction of endogenous and exogenous oscillations (Nuidel et al., 2019). When combining resonance scanning with EEG-controlled adaptive neurostimulation, significant positive effects in the treatment of patients with post-Covid syndrome were registered after only a single combined stimulation (Polevaya et al., 2022).

Conclusion

The presented data allow us to conclude that the creation and improvement of methods of non-invasive brain stimulation is an actively developing and promising area of neurophysi-ology. Judging by the reviewed publications, the greatest development and effectiveness are

demonstrated by methods using multimodal sensory stimulation, taking into account functional brain imaging data. A particularly promising line of research seems to be the automatic modulation of non-invasive sensory influences by feedback signals from a person's own rhythmic processes - breathing rhythm, heartbeat rhythm and EEG rhythms. The complex feedback from these rhythms promotes the involvement of interoceptive signals that are meaningful to humans into the mechanisms of multisen-sory integration, neuroplasticity and resonance mechanisms of the brain. Thanks to the use of control signals from endogenous rhythms, such non-invasive stimulation, by taking into account the dynamics of brain microstates, achieves high personalization and effectiveness of therapeutic interventions. Automatic control of therapeutic sensory influences makes it possible to use methods of adaptive neurostimulation with feedback in conditions that do not require conscious efforts of the subjects, which is especially important when conducting therapeutic sessions with children and patients who are characterized by altered mental states or drug therapy is contraindicated.

The listed advantages of adaptive neurostimulation methods with feedback open up prospects for their use in a wide range of rehabilitation activities, in educational institutions to enhance human cognitive activity and learning processes, in military and sports medicine, disaster medicine, and scientific research.

The research was funded by the Russian Scientific Foundation, grant No. 22-18-20075.

The authors declare that there is no conflict of interest.

References

ANTAL A., LUBER B., BREM A.K., et al. (2022): Non-invasive brain stimulation and neuroenhancement.

Clin NeurophysiolPract. 7, 146-165. doi: 10.1016/j.cnp.2022.05.002. ASHANINA E.N. & SENIK M.N. (2018): Modern research into audio-visual impact techniques (review of

domestic and foreign literature for 2011-2018). Bulletin of psychotherapy 67(72), 44-65. BAKULIN I S., POYDASHEVA A G., PAVLOV N.A., SUPONEVA N.A., PIRADOV M.A. & AFTANAS L.I. (2019): Transcranial electrical stimulation in improving hand function in stroke. Advances in physiological sciences 50(1), 90-104. doi: 10.1134/S030117981901003X.

BAKULIN IS., POYDASHEVA AG., MEDINTSEV A.A., SUPONEVA N.A. & PIRADOV M.A. (2020): Transcranial magnetic stimulation in cognitive neuroscience: methodological foundations and safety. Russian Journal of Cognitive Science 7(3), 25-44. doi: 10.47010/20.3.2.

BAKULIN I S., POYDASHEVA A G., LAGODA D.YU., SUPONEVA N.A. & PIRADOV M.A. (2021): Prospects for the development of therapeutic transcranial magnetic stimulation. Nervous diseases 4, 3-10. doi: 10.24412/2226-0757-2021-12371.

BEGEMANN M.J., BRAND B.A., CURCIC-BLAKE B., ALEMAN A. & SOMMER I.E. (2020): Efficacy of non-invasive brain stimulation on cognitive functioning in brain disorders: a meta-analysis. Psychol Med. 50(15), 2465-2486. doi: 10.1017/S0033291720003670.

BELOPASOVA A.V., DOBRYNINA L.A., KADYKOV AS., BERDNIKOVICH E.S., BERGELSON G.M. & TSYPUSHTANOVA M.M. (2020): Non-invasive brain stimulation in the rehabilitation of patients with post-stroke aphasia. Journal Neurol Psychiatry S.S. Korsakov 120(3-2), 23-28. doi: 10.17116/jnevro202012003223.

BERGMANN T O. (2018): Brain State-Dependent Brain Stimulation. Front Psychol. 9, 2108. doi: 10.3389/fpsyg.2018.02108.

BRADLEY C., NYDAM A S., DUX P.E. & MATTINGLEY J.B. (2022): State-dependent effects of neural stimulation on brain function and cognition. Nat Rev Neurosci. 23(8), 459-475. doi: 10.1038/s41583-022-00598-1.

BURKE M.J., FRIED P.J. & PASCUAL-LEONE A. (2019): Transcranial magnetic stimulation: Neurophys-iological and clinical applications. Handb Clin Neurol. 163, 73-92. doi: 10.1016/B978-0-12-804281-6.00005-7.

CHINO T., KINOSHITA S. & ABO M. (2023): Repetitive Transcranial Magnetic Stimulation and Rehabilitation Therapy for Upper Limb Hemiparesis in Stroke Patients: A Narrative Review. Prog Rehabil Med. 8, 20230005. doi: 10.2490/prm.20230005.

DE BOCK R., MACKINTOSH A.J., MAIER F., BORGWARDT S., RIECHER-ROSSLER A. & AN-DREOU C. (2020): EEG microstates as biomarker for psychosis in ultra-high-risk patients. Transl Psychiatry 10(1), 300. doi: 10.1038/s41398-020-00963-7.

DE LUCA R., POLLICINO P., RIFICI C., DE COLA C., BILLERI L., MARINO S., TRIFIRO S., FIUMARA E., RANDAZZO M., BRAMANTI P. & TORRISI M. (2021): Improving motor and cognitive recovery following severe traumatic brain injury using advanced emotional audio-video stimulation: Lessons from a case report. Medicine (Baltimore) 100(31), e26685. doi: 10.1097/MD.0000000000026685.

DICK O.E. & NOZDRACHEV A.D. (2020): Dynamics of patterns of electrical activity of the brain during disorders of its functional state. Uspekhi Fiziol. Sci. 51(2), 68-87. doi: 10.31857/S0301179820020046.

DING Z., WANG Y., LI J. & LI X. (2022): Closed-loop TMS-EEG reactivity with occipital alpha-phase synchronized. J Neural Eng. 19(5). doi: 10.1088/1741-2552/ac9432.

DOBRUSHINA O.R., DOBRYNINA L.A., ARINA G.A., et al. (2020): The relationship between interocep-tive perception and emotional intelligence: a functional neuroimaging study. Journal of higher nervous activity I.P. Pavlov 70(2), 206-216. doi: 10.31857/S0044467720020069.

DRAAISMA L.R., WESSEL M.J. & HUMMEL F.C. (2020): Non-invasive brain stimulation to enhance cognitive rehabilitation after stroke. Neurosci Lett. 719, 133678. doi: 10.1016/j.neulet.2018.06.047.

FARKHONDEH T.N.F., HEYSIEATTALAB S., RAMANATHAN D.S., RAOUFY M.R. & NAZARI M.A. (2022): Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs. Neuroscience 483, 104-126. doi: 10.1016/j.neurosci-ence.2021.12.004.

FEDOTCHEV A.I. (1996): Endogenous rhythms of the body as a factor in the modulation of stimulation parameters. Biophysics 41(3), 718-722.

FEDOTCHEV A.I., KRUK V.M. & SEMIKIN G.I. (2019b): Functional reliability of a specialist: modern risks and opportunities to eliminate them. Uspekhi Fiziol. Sci. 50(3), 1-11. doi: 10.1134/S0301179819030044.

FEDOTCHEV A.I., PARIN S B. & POLEVAYA S.A. (2021b): Neurointerfaces based on endogenous rhythms of the body to optimize the functional state of a person and his cognitive rehabilitation. Uspekhi Fiziol. Sci. 52(2), 83-92. doi: 10.31857/S030117982102003X.

FEDOTCHEV A.I., PARIN S.B. & POLEVAYA S.A. (2021c): Adaptive Neurostimulation Methods in Correcting Posttraumatic Stress Disorder and Professional Burnout Syndrome. Opera Med Physiol. 8(2), 68-74. doi: 10.24412/2500-2021-2-68-74.

FEDOTCHEV A., PARIN S. & POLEVAYA S. (2023): Resonance scanning as an efficiency enhancer for EEG-guided adaptive neurostimulation. Life 13(620), 1-9. doi: 10.3390/life13030620.

FEDOTCHEV A.I., PARIN S.B., POLEVAYA S.A. & ZEMLYANAYA A.A. (2021a): Human endogenous rhythms in the development of non-invasive methods of closed-loop adaptive neurostimulation. J. PersMed. 11, 437. doi: 10.3390/jpm11050437.

FEDOTCHEV A., PARIN S., POLEVAYA S. & ZEMLIANAIA A. (2022): EEG-based musical neurointerfaces in the correction of stress-induced states. Brain Comput. Interfaces 9, 1-6. doi: 10.1080/2326263X2021.1964874.

FEDOTCHEV A., RADCHENKO G. & ZEMLIANAIA A. (2018): On one approach to health protection: Music of the brain. J Integr Neurosci. 17(3-4), 309-315. doi: 10.3233/JIN-170053.

FEDOTCHEV A.I., ZEMLYANAYA A.A., SAVCHUK L.V. & POLEVAYA S.A. (2019a): Neurointerface with double feedback from EEG in the correction of stress-induced disorders. Modern technologies in medicine 11(1), 150-154. doi: 10.17691/stm2019.11.1.17.

FÖLDI T., LÖRINCZ M L. & BERENYI A. (2021): Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond. Front Neural Circuits 15, 784085. doi: 10.3389/fncir.2021.784085.

GEISER T., HERTENSTEIN E., FEHER K., MAIER J.G., SCHNEIDER C.L., ZÜST M.A., WUNDERLIN M., MIKUTTA C., KLÖPPEL S. & NISSEN C. (2020): Targeting Arousal and Sleep through Noninvasive Brain Stimulation to Improve Mental Health. Neuropsychobiology 79(4-5), 284-292. doi: 10.1159/000507372.

GENTSCH A., SEL A., MARSHALL A.C. & SCHÜTZ-BOSBACH S. (2019): Affective interoceptive inference: Evidence from heart-beat evoked brain potentials. Hum Brain Mapp. 40(1), 20-33. doi: 10.1002/hbm.24352.

GIBSON J. (2019): Mindfulness, Interoception, and the Body: A Contemporary Perspective. Front Psychol. 10, 2012. doi: 10.3389/fpsyg.2019.02012.

GOLOVIN M.S., BALIOZ N.V., KRIVOSHCHEKOV S.G. & AIZMAN R.I. (2018): Integration of functional, psychophysiological and biochemical processes in the body of athletes after audiovisual stimulation. Human physiology 44(1), 64-71. doi: 10.7868/S0131164618010083.

GONSALVEZ I., SPAGNOLO P., DWORETZKY B. & BASLET G. (2021): Neurostimulation for the treatment of functional neurological disorder: A systematic review. Epilepsy Behav Rep. 16, 100501. doi: 10.1016/j.ebr.2021.100501.

GRECHKO A.V., SHEVTSOVA E E., KOVALEVA G.A. & RODIONOVA A.D. (2018): Variability in the use of sensory stimulation methods in the rehabilitation of patients with minimal manifestations of consciousness. Bulletin of restorative medicine 2(84), 129-135.

HAO W., LIU Y., GAO Y., GONG X. & NING Y. (2023): Transcranial direct current stimulation for the treatment of post-stroke depression: A systematic review. Front Neurol. 13, 955209. doi: 10.3389/fneur.2022.955209.

HOSAIN M.K., KOUZANI A. & TYE S. (2014): Closed loop deep brain stimulation: an evolving technology. Australas Phys Eng Sci Med. 37(4), 619-634. doi: 10.1007/s13246-014-0297-2.

HU W., ZHANG Z., ZHAO H., ZHANG L., LI L., HUANG G. & LIANG Z. (2023): EEG microstate correlates of emotion dynamics and stimulation content during video watching. Cereb Cortex 33(3), 523542. doi: 10.1093/cercor/bhac082.

JANGWAN N.S., ASHRAF GM., RAM V., SINGH V., ALGHAMDI B S., ABUZENADAH A.M. & SINGH M.F. (2022): Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects. Front Syst Neurosci. 16, 1000495. doi: 10.3389/fnsys.2022.1000495.

JANSSENS S.E.W. & SACK A.T. (2021): Spontaneous Fluctuations in Oscillatory Brain State Cause Differences in Transcranial Magnetic Stimulation Effects Within and Between Individuals. Front Hum Neurosci. 15, 802244. doi: 10.3389/fnhum.2021.802244.

KAN R.L.D., ZHANG B.B.B., ZHANG J.J.Q. & KRANZ G S. (2020): Non-invasive brain stimulation for posttraumatic stress disorder: a systematic review and meta-analysis. Transl Psychiatry 10(1), 168. doi: 10.1038/s41398-020-0851-5.

KARRI J., LI S., ZHANG L., CHEN Y.T., STAMPAS A. & LI S. (2018): Neuropathic pain modulation after spinal cord injury by breathing-controlled electrical stimulation (BreEStim) is associated with restoration of autonomic dysfunction. J Pain Res. 11, 2331-2341. doi: 10.2147/JPR.S174475.

KARRI J., LI S., CHEN Y.T., STAMPAS A. & LI S. (2021): Observations of Autonomic Variability Following Central Neuromodulation for Chronic Neuropathic Pain in Spinal Cord Injury. Neuromodulation 24(3), 427-433. doi: 10.1111/ner.12979.

KASTEN F.H. & HERRMANN C.S. (2022): The hidden brain-state dynamics of tACS aftereffects. Neuroimage 264, 119713. doi: 10.1016/j.neuroimage.2022.119713.

KHRULEV A.E., KURYATNIKOVA K.M., BELOVA A.N., POPOVA P.S. & KHRULEV S.E. (2022): Modern technologies for the rehabilitation of patients with motor disorders in the early recovery period of cerebral stroke (review). Modern technologies in medicine 14(6), 64-78. doi: 10.17691/stm2022.14.6.07.

KHALSA S.S., ADOLPHS R., CAMERON O.G., et al. (2018): Interoception and Mental Health: A Roadmap. Biol Psychiatry Cogn Neurosci Neuroimaging 3(6), 501-513. doi: 10.1016/j.bpsc.2017.12.004.

KOENIG T., SMAILOVIC U. & JELIC V. (2020): Past, present and future EEG in the clinical workup of dementias. Psychiatry Res Neuroimaging 306, 111182. doi: 10.1016/j.pscychresns.2020.111182.

KOTELNIKOVA A V., KUKSHINA A.A., TUROVA E.A. & TIKHONOVA A S. (2021): Binaural acoustic beats in psychological rehabilitation of patients with impaired motor functions. Bulletin of restorative medicine 20(1), 60-69. doi: 10.38025/2078-1962-2021-20-1-60-69.

KOTOV S.V., ISAKOVA E.V., ZAITSEVA E.V. & EGOROVA YU.V. (2020): Multimodal stimulation in neurorehabilitation of patients with post-stroke cognitive impairment. Journal of Neurology and Psychiatry S.S. Korsakov 120(5), 125-130. doi: 10.17116/jnevro2020120051125.

LAGODA D.YU., DOBRYNINA L.A., SUPONEVA N.A., BAKULIN I S., POYDASHEVA AG., TSYPUSHTANOVA M M., KADYKOV A S. & PIRADOV M.A. (2021): Rhythmic transcranial magnetic stimulation in the treatment of moderate cognitive impairment in cerebral microangiopathy. Annals of Clinical and Experimental Neurology 15(4), 5-14. doi: 10.54101/ACEN.2021.4.1.

NASR K., HASLACHER D., DAYAN E., CENSOR N., COHEN L.G. & SOEKADAR S.R. (2022): Breaking the boundaries of interacting with the human brain using adaptive closed-loop stimulation. Prog Neurobiol. 216, 102311. doi: 10.1016/j.pneurobio.2022.102311.

NGO H.V., SEIBOLD M., BOCHE D C., MÖLLE M. & BORN J. (2019): Insights on auditory closed-loop stimulation targeting sleep spindles in slow oscillation up-states. J Neurosci Methods 316, 117-124. doi: 10.1016/j.jneumeth.2018.09.006.

NUMSSEN O., VAN DER BURGHT C.L. & HARTWIGSEN G. (2023): Revisiting the focality of noninvasive brain stimulation - Implications for studies of human cognition. Neurosci Biobehav Rev. 149, 105154. doi: 10.1016/j.neubiorev.2023.105154.

NUYDEL I.V., KOLOSOV A V., DEMAREVA V.A. & YAKHNO V.G. (2019): Application of a phenom-enological mathematical model to reproduce the effect of interaction between endogenous and exogenous oscillations during neurofeedback. Modern Technologies in Medicine 11(1), 103-108. doi: 10.17691/stm2019.11.1.12.

OXLEY T. & OPIE N. (2019): Closed-Loop Neuromodulation: Listen to the Body. World Neurosurg. 122, 415-416. doi: 10.1016/j.wneu.2018.11.132.

PATS N.V. & GORYUNOVA V.V. (2018): New approaches to the prevention of fatigue in students using audiovisual stimulation. Human health, theory and methodology of physical culture and sports 2(9), 102-112.

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

QIAO J., WANG Y. & WANG S. (2022): Natural frequencies of neural activities and cognitions may serve as precise targets of rhythmic interventions to the aging brain. Front Aging Neurosci. 14, 988193. doi: 10.3389/fnagi.2022.988193.

QUADT L., CRITCHLEY H.D. & GARFINKEL S.N. (2018): The neurobiology of interoception in health and disease. Ann N Y Acad Sci. 1428(1), 112-128. doi: 10.1111/nyas.13915.

REBELLO-SANCHEZ I., VASQUEZ-AVILA K., PARENTE J., PACHECO-BARRIOS K., DE MELO P.S., TEIXEIRA P.E.P., JONG K., CAUMO W. & FREGNI F. (2022): Insights and Future Directions on the Combined Effects of Mind-Body Therapies with Transcranial Direct Current Stimulation: An Evidence-based Review. JIntSocPhysRehabilMed. 5(4), 129-148. doi: 10.4103/ijprm.jisprm-000167.

ROY C., DALLA BELLA S., PLA S. & LAGARDE J. (2021): Multisensory integration and behavioral stability. Psychol Res. 85(2), 879-886. doi: 10.1007/s00426-019-01273-4.

RUCH S., SCHMIDIG F.J., KNÜSEL L. & HENKE K. (2022): Closed-loop modulation of local slow oscillations in human NREM sleep. Neuroimage 264, 119682. doi: 10.1016/j.neuroimage.2022.119682.

SCHNEIDER J., LEWIS P.A., KOESTER D., BORN J. & NGO H.V. (2020): Susceptibility to auditory closed-loop stimulation of sleep slow oscillations changes with age. Sleep 43(12), zsaa111. doi: 10.1093/sleep/zsaa111.

SCHUTTER D.J.L.G., SMITS F. & KLAUS J. (2023): Mind matters: A narrative review on affective state-dependency in non-invasive brain stimulation. Int J Clin Health Psychol. 23(3), 100378. doi: 10.1016/j .ijchp.2023.100378.

SHALTOUT H.A., LEE S.W., TEGELER C.L., HIRSCH J R., SIMPSON S.L., GERDES L. & TEGELER C.H. (2018): Improvements in Heart Rate Variability, Baroreflex Sensitivity, and Sleep After Use of Closed-Loop Allostatic Neurotechnology by a Heterogeneous Cohort. Front Public Health 6, 116. doi: 10.3389/fpubh.2018.00116.

SHCHEGOLKOV A.M., ALEKHNOVICH A.V., TIMERGAZINA E.Z., DYBOV M.D. & MASSALSKY R.I. (2022): The influence of bioacoustic correction on the process of medical rehabilitation of patients with consequences of transient cerebrovascular disorders (review). Hospital medicine: science and practice 5(4), 46-49. doi: 10.34852/GM3CVKG.2022.17.46.009.

STANYER EC., BANIQUED P.D.E., AWAIS M., KOUARA L., DAVIES AG., KILLAN EC. & MUSH-TAQ F. (2022): The impact of acoustic stimulation during sleep on memory and sleep architecture: A meta-analysis. J Sleep Res. 31(3), e13385. doi: 10.1111/jsr.13385.

STEFANOU M.I., BAUR D., BELARDINELLI P., BERGMANN TO., BLUM C., GORDON P.C., NIEMINEN J.O., ZRENNER B., ZIEMANN U. & ZRENNER C. (2019): Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation. J Vis Exp. 150. doi: 10.3791/59711.

SUN F.T. & MORRELL M.J. (2014): Closed-loop neurostimulation: the clinical experience. Neurotherapeu-tics 11(3), 553-563. doi: 10.1007/s13311-014-0280-3.

SYSOEV V.N., CHEBYKINA A V., DUSHKINA M.A. & DERGACHEV V.B. (2018): Evaluation of the effectiveness of using a single session of audiovisual stimulation to correct the functional state of the body. Bulletin of the Russian Military Medical Academy 3(63), 128-132.

VOSSKUHL J., STRÜBER D. & HERRMANN C.S. (2018): Non-invasive Brain Stimulation: A Paradigm Shift in Understanding Brain Oscillations. Front Hum Neurosci. 12, 211. doi: 10.3389/fnhum.2018.00211.

WEISS E., KANN M. & WANG Q. (2023): Neuromodulation of Neural Oscillations in Health and Disease. Biology (Basel) 12(3), 371. doi: 10.3390/biology12030371.

WENDT K., DENISON T., FOSTER G., KRINKE L., THOMSON A., WILSON S. & WIDGE AS. (2022): Physiologically informed neuromodulation. J Neurol Sci 434, 120121. doi: 10.1016/j jns.2021.120121.

WUNDERLIN M., ZÜST M.A., HERTENSTEIN E., FEHÉR K.D., SCHNEIDER C.L., KLÖPPEL S. & NISSEN C. (2021): Modulating overnight memory consolidation by acoustic stimulation during slow-wave sleep: a systematic review and meta-analysis. Sleep 44(7), zsaa296. doi: 10.1093/sleep/zsaa296.

YANG H., LUO Y., HU Q., TIAN X. & WEN H. (2021): Benefits in Alzheimer's Disease of Sensory and Multisensory Stimulation. J Alzheimers Dis 82(2), 463-484. doi: 10.3233/JAD-201554.

YU B., FUNK M., HU J. & FEIJS L. (2018): Unwind: A musical biofeedback for relaxation assistance. Behav. Inf. Technol 37, 800-814. doi: 10.1080/0144929X.2018.1484515.

ZANOS S. (2019): Closed-Loop Neuromodulation in Physiological and Translational Research. Cold Spring Harb PerspectMed 9(11), a034314. doi: 10.1101/cshperspect.a034314.

ZHOU X. & MILLER J.P. (2019): Commentary: The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation. Neurosurgery 85(3), E440-E441. doi: 10.1093/neuros/nyz097.

ZRENNER B., ZRENNER C., GORDON P.C., BELARDINELLI P., MCDERMOTT E.J., SOEKADAR SR., FALLGATTER A.J., ZIEMANN U. & MÜLLER-DAHLHAUS F. (2020): Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-trig-gered TMS. Brain Stimul. 13(1), 197-205. doi: 10.1016/j.brs.2019.10.007.

i Надоели баннеры? Вы всегда можете отключить рекламу.