Russian Journal of PhysiologyRussian Journal of PhysiologyРоссийский физиологический журнал им. И.М. Сеченова0869-81392658-655XThe Russian Academy of Sciences65162210.31857/S0869813924080017BDDGPYEXPERIMENTAL ARTICLESЭКСПЕРИМЕНТАЛЬНЫЕ СТАТЬИResearch ArticleEEG sensorimotor rhythms dynamics in children with cerebral palsy during the course of neurorehabilitation, depending in the success of their imagination of movementsДинамика сенсомоторных ритмов ЭЭГ у детей с церебральным параличом при прохождении курса нейрореабилитации в зависимости от успешности представления ими движенийPavlenkoV. B.ПавленкоВ. Б.
Russian Federation
vpav55@gmail.comVlasenkoS. V.ВласенкоС. В.
Russian Federation
vpav55@gmail.comChuyanE. N.ЧуянЕ. Н.
Russian Federation
vpav55@gmail.comKaidaA. I.КайдаА. И.
Russian Federation
vpav55@gmail.comOrekhovaL. S.ОреховаЛ. С.
Russian Federation
vpav55@gmail.comBirukovaE. A.БирюковаЕ. А.
Russian Federation
vpav55@gmail.comPavlenkoD. V.ПавленкоД. В.
Russian Federation
vpav55@gmail.comTatarisS. E.ТатарисШ. Э.
Russian Federation
vpav55@gmail.comVernadsky Crimean Federal UniversityКрымский федеральный университет им. В. И. ВернадскогоResearch institute of children's balneology, physiotherapy and medical rehabilitationНаучно-исследовательский институт детской курортологии, физиотерапии и медицинской реабилитации1508202411081223123701022025Copyright ©; 2024, Russian Academy of SciencesCopyright ©; 2024, Российская академия наук2024Russian Academy of SciencesРоссийская академия наукhttps://journals.eco-vector.com/0869-8139/article/view/651622

An analysis of changes in sensorimotor rhythms of the electroencephalogram (EEG) and features of the restoration of motor functions during a course of neurorehabilitation using a non-invasive brain-computer-hand exoskeleton interface was carried out in 50 children aged 7–15 years of both sexes suffering from cerebral palsy (CP). EEG was recorded in 32 leads under conditions of rest and kinesthetic imagination of hand extension movements. Depending on the success of the classifier program in determining imaginary states based on the EEG pattern, the children were divided into two groups – with high and low success when imagining movements. In children of the studied groups, when undergoing a course of neurorehabilitation, differences were revealed in the nature of changes in EEG amplitude in the sensorimotor mu (8–13 Hz) and beta (15–25 Hz) rhythms frequency ranges. When imagining movements of the right hand, intergroup differences for the mu rhythm reached the level of statistical significance in the medial electrodes of the fronto-central and parietal areas of the neocortex, for the beta rhythm – in the medial lead of the parietal region. Children in the group with high success showed a decrease or slight increase in the amplitude of the mu- and beta rhythm in these brain areas at the last session of the course compared to the first. We hypothesize that children in this group are not only better able to kinesthetically imagine movements throughout the course of neurorehabilitation, but also, based on feedback signals, learn to effectively adjust their strategies for movement imagination. Children in the group with low success showed an increase in the amplitude of sensorimotor rhythms in these brain areas, indicating the development of inhibition in the frontoparietal motor network. Patients, whose conditions were determined more accurately by the classifier, achieved higher rates of motor rehabilitation. The results of the study are important for clarifying the brain mechanisms of motor functions restoration in patients with cerebral palsy under the influence of a course of neurorehabilitation.

У 50 детей 7–15 лет обоего пола, страдающих детским церебральным параличом (ДЦП), проведен анализ изменений электроэнцефалограммы (ЭЭГ) в частотных диапазонах сенсомоторных мю- (8–13 Гц) и бета- (15–25 Гц) ритмов и особенностей восстановления двигательных функций в процессе курса нейрореабилитации из 10 сеансов с применением неинвазивного интерфейса «мозг – компьютер – экзоскелет кисти». ЭЭГ регистрировали в 32 отведениях в условиях покоя и кинестетического воображения движений разгибания кистей рук. В зависимости от успешности определения программой-классификатором воображаемых состояний по паттерну ЭЭГ дети были разделены на две группы – с высокой и низкой успешностью представления движений. У детей исследованных групп при прохождении курса нейрореабилитации выявлены отличия в характере изменений амплитуды ЭЭГ в частотных диапазонах указанных ритмов. При представлении движений правой руки межгрупповые различия для мю-ритма достигали уровня статистической значимости в сагиттальных отведениях лобно-центральных и теменных областей неокортекса, для бета-ритма – в сагиттальном лобно-центральном отведении. У детей группы с низкой успешностью в указанных областях на последнем сеансе курса по сравнению с первым наблюдался рост амплитуды сенсомоторных ритмов, свидетельствующий о развитии торможения в лобно-теменной двигательной сети. У детей группы с высокой успешностью в этих областях амплитуды мю- и бета-ритма к последнему сеансу не менялись, т. е. степень синхронизации/десинхронизации сенсомоторных ритмов при представлении движений в процессе курса нейрореабилитации оставалась стабильной. Пациенты, состояния которых классификатор определял более точно, достигли более высоких показателей реабилитации двигательной сферы. Результаты исследования важны для уточнения мозговых механизмов восстановления двигательных функций у пациентов с ДЦП под влиянием курса нейрореабилитации.

cerebral palsyelectroencephalogrambrain-computer interfacemu- and beta rhythmneurorehabilitationдетский церебральный параличэлектроэнцефалограммаинтерфейс мозг – компьютермю- и бета-ритмнейрореабилитацияРоссийский научный фондRussian Science Foundation22–15–20035This work was funded by the Russian Science Foundation and the Republic of Crimea grant No. 22–15–20035, https://rscf.ru/project/22–15–20035/. No additional grants were received for conducting or supervising this specific study.Данная работа финансировалась за счет средств гранта Российского научного фонда и Республики Крым № 22–15–20035, https://rscf.ru/project/22–15–20035/. Никаких дополнительных грантов на проведение или руководство данным конкретным исследованием получено не было.McIntyre S, Goldsmith S, Webb A, Ehlinger V, Hollung SJ, McConnell K, Arnaud C, Smithers-Sheedy H, Oskoui M, Khandaker G, Himmelmann K (2022) Global prevalence of cerebral palsy: A systematic analysis. Dev Med Child Neurol 64(12): 1494–1506. http://doi.org/10.1111/dmcn.15346Démas J, Bourguignon M, Périvier M, De Tiège X, Dinomais M, Van Bogaert P (2020) Mu rhythm: State of the art with special focus on cerebral palsy. Ann Phys Rehabil Med 63(5): 439–446. https://doi.org/10.1016/j.rehab.2019.06.007Aisen ML, Kerkovich D, Mast J, Mulroy S, Wren TA, Kay RM, Rethlefsen SA (2011) Cerebral palsy: clinical care and neurological rehabilitation. Lancet Neurol 10(9): 844–852. https://doi.org/10.1016/S1474–4422(11)70176–4Sadowska M, Sarecka-Hujar B, Kopyta I (2020) Cerebral Palsy: Current Opinions on Definition, Epidemiology, Risk Factors, Classification and Treatment Options. Neuropsychiatr Dis Treat 16: 1505–1518. https://doi.org/10.2147/NDT.S235165Patel DR, Neelakantan M, Pandher K, Merrick J (2020) Cerebral palsy in children: a clinical overview. Transl Pediatr 9(1): 125–135. https://doi.org/10.21037/tp.2020.01.01Ашрафова УШ, Куприянова ОС, Кармазина ЕК, Клочкова ОА, Мамедъяров АМ, Комарова ЕВ, Ивардава МИ, Каркашадзе ГА (2023) Персонализированный подход к применению методов роботизированной механотерапии у детей с церебральным параличом разных возрастных групп: обзор литературы. Педиатр фармакол 20(6): 588–596. [Ashraphova USh, Kupriianova OS, Karmazina EK, Klochkova OA, Mamedieiarov AM, Komarova EV, Ivardava MI, Karkashadze GA (2023) A personalized approach to application of robotic mechanotherapy methods in children with cerebral palsy of different age groups (review). Pediatr farmakol 20(6): 588–596. (In Russ)]. https://doi.org/10.15690/pf.v20i6.2668Errante A, Di Cesare G, Pinardi C, Fasano F, Sghedoni S, Costi S, Ferrari A, Fogassi L (2019) Mirror Neuron System Activation in Children With Unilateral Cerebral Palsy During Observation of Actions Performed by a Pathological Model. Neurorehabil Neural Repair 33(6): 419–431. https://doi.org/10.1177/1545968319847964Xie J, Jiang L, Li Y, Chen B, Li F, Jiang Y, Gao D, Deng L, Lv X, Ma X, Yin G, Yao D, Xu P (2021) Rehabilitation of motor function in children with cerebral palsy based on motor imagery. Cogn Neurodyn 15(6): 939–948. https://doi.org/10.1007/s11571–021–09672–3Xie YL, Yang YX, Jiang H, Duan XY, Gu LJ, Qing W, Zhang B, Wang YX (2022) Brain–machine interface-based training for improving upper extremity function after stroke: a meta-analysis of randomized controlled trials. Front Neurosci 16: 949575. https://doi.org/10.3389/fnins.2022.949575Fedotova IR, Bobrov PD (2022) Foundation and aspects of using motor imagery and brain computer interfaces in rehabilitation of children with cerebral palsy. IP Pavlov J Higher Nerv Activity 72(1): 87‒99. https://doi.org/ 10.31857/S004446772201004X.Ларина НВ, Корсунская ЛЛ, Власенко СВ (2019) Комплекс «Экзокисть-2» в реабилитации верхней конечности при детском церебральном параличе с использованием неинвазивного интерфейса «мозг‒компьютер». Нервно-мышечн болезни 11(4): 12‒20. [Larina NV, Korsunskaya LL, Vlasenko SV (2019) The “Exo hand-2” complex in the rehabilitation of the upper limb in cerebral palsy using the non-invasive interface “brain-computer”. Nervno-myshechn bolezni 11(4): 12‒20. (In Russ)]. https://doi.org/10.17650/2222–8721–2019–9–4–44–50Bobrov PD, Biryukova EV, Polyaev BA, Lajsheva OA, Usachjova EL, Sokolova AV, Mikhailova DI, Dement'eva KN, Fedotova IR (2020) Rehabilitation of patients with cerebral palsy using hand exoskeleton controlled by brain-computer interface. Bull RSMU4: 33–40. https://doi.org/10.24075/brsmu.2020.047Pavlenko VB, Vlasenko SV, Orekhova LS, Biryukova EA (2023) Speech improvement in children with cerebral palsy by "brain-computer-hand exoskeleton" neurointerface rehabilitation. Bull RSMU (4): 62–67. https://doi.org/10.24075/brsmu.2023.02614 Kulak W, Sobaniec W, Kuzia JS, Boćkowski L (2006) Neurophysiologic and neuroimaging studies of brain plasticity in children with spastic cerebral palsy. Exp Neurol 198(1): 4–11. http://doi.org/10.1016/j.expneurol.2005.11.014Jenson D, Bowers AL, Hudock D, Saltuklaroglu T (2020) The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering. Front Hum Neurosci 10(13): 458. http://doi.org/10.3389/fnhum.2019.00458Hari R, Salmelin R (1997) Human cortical oscillations: a neuromagnetic view through the skull. Trends Neurosci 20: 44–49. http://doi.org/10.1016/S0166–2236(96)10065–5Stolk A, Brinkman L, Vansteensel MJ, Aarnoutse E, Leijten FS, Dijkerman CH, Knight RT, de Lange FP, Toni I (2019) Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system. Elife 8: e48065. https://doi.org/10.7554/eLife.48065Angelini M, Fabbri-Destro M, Lopomo NF, Gobbo M, Rizzolatti G, Avanzini P (2018) Perspective-dependent reactivity of sensorimotor mu rhythm in alpha and beta ranges during action observation: an EEG study. Scient Rep 8(1): 1–11. https://doi.org/10.1038/s41598–018–30912-wLarionova EV, Garakh ZhV, Zaytseva YS (2022) Mu-rhythm in contemporary research: theoretical and methodological aspects. IP Pavlov J Higher Nerv Activity 72(1): 11–35. https://doi.org/ 10.31857/S0044467722010051Kukke SN, de Campos AC, Damiano D, Alter KE, Patronas N, Hallett M (2015) Cortical activation and inter-hemispheric sensorimotor coherence in individuals with arm dystonia due to childhood stroke. Clin Neurophysiol 126(8): 1589–98. http://doi.org/10.1016/j.clinph.2014.11.002Hinchberger V, Kang SH, Kline J, Stanley CJ, Bulea TC, Damiano DL (2023) Investigation of brain mechanisms underlying upper limb function in bilateral cerebral palsy using EEG. Clin Neurophysiol 151: 116–127. http://doi.org/10.1016/j.clinph.2023.04.006Phillips C, Kline J, Stanley CJ, Bulea TC, Damiano DL (2023) Children With Bilateral Cerebral Palsy Exhibit Bimanual Asymmetric Motor Deficits and EEG Evidence of Dominant Sensorimotor Hemisphere Overreliance During Reaching. Neurorehabil Neural Repair 37(9): 617–627. https://doi.org/10.1177/15459683231195044Galli J, Garofalo G, Brunetti S, Loi E, Portesi M, Pelizzari G, Rossi A, Fazzi E, Buccino G (2022) Children with Cerebral Palsy can imagine actions like their normally developed peers. Front Neurol 6(13): 951152. https://doi.org/10.3389/fneur.2022.951152Larina NV, Nacharova MA, Korsunskaya LL, Vlasenko SV, Pavlenko VB (2020) Changes in EEG patterns in the α-frequency band following BCI-based therapy in children with cerebral palsy. Bull RSMU (4): 41–46. http://doi.org/10.24075/brsmu.2020.043Congedo M, Barachant A, Bhatia R (2017) Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput Interfac 4(3): 155–174. https://doi.org/10.1080/2326263X.2017.1297192Raymaekers R, Wiersema JR, Roeyers H (2009) EEG study of the mirror neuron system in children with high functioning autism. Brain Res 1304: 113–121. https://doi.org/10.1016/j.brainres.2009.09.068Mahoney FI, Barthel DW (1965) Functional evaluation: The Barthel index. Md State Med J 14: 61–65.Супонева НА, Юсупова ДГ, Ильина КА, Мельченко ДА, Бутковская АА, Жирова ЕС, Таратухина АС, Зимин АА, Зайцев АБ, Клочков АС, Люкманов РХ, Котовсмоленский АМ, Хижникова АЕ, Гатина ГА, Кутлубаев МА, Пирадов МА (2020) Валидация Модифицированной шкалы Эшворта (Modified Ashworth Scale) в России. Анналы клин эксперим неврол 14(1): 89–96. [Suponeva NA, Yusupova DG, Ilyina KA, Melchenko DA, Butkovskaya AA, Zhirova ES, Taratukhina AS, Zimin AA, Zaitsev AB, Klochkov AS, Lyukmanov RKh, Kotov-Smolensky AM, Khizhnikova AE, Gatina GA, Kutlubaev MA, Piradov MA (2020) Validation of the Modified Ashworth scale in Russia. Ann klin eksper nevrol 14(1): 89–96. (In Russ)]. https://doi.org/10.25692/acen.2020.1.10Eliasson AC, Krumlinde-Sundholm L, Rösblad B, Beckung E, Arner M, Ohrvall AM, Rosenbaum P (2006) The Manual Ability Classification System (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability. Dev Med Child Neurol 48(7): 549–554.Miziara IM, Lopes JBP, Lazzari RD, de Moura RCF, Albuquerque MB, Oliveira C, Navesa ELM (2019) Comparison of MU rhythm desynchronisation between children with right-side and left-side hemiparetic cerebral palsy during a voluntary movement: case study. Gait & Posture 73(1): 306–307. https://doi.org/10.1016/j.gaitpost.2019.07.176Jongsma MLA, Steenbergen B, Baas CM, Aarts PB, van Rijn CM (2020) Lateralized EEG mu power during action observation and motor imagery in typically developing children and children with unilateral Cerebral Palsy. Clin Neurophysiol 131(12): 2829–2840. http://doi.org/10.1016/j.clinph.2020.08.022Koessler L, Maillard L, Benhadid A, Vignal JP, Felblinger J, Vespignani H, Braun M (2009) Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. Neuroimage 46(1): 64–72. http://doi.org/10.1016/j.neuroimage.2009.02.006Shin YK, Lee DR, Hwang HJ, You SJ, Im CH (2012) A novel EEG-based brain mapping to determine cortical activation patterns in normal children and children with cerebral palsy during motor imagery tasks. NeuroRehabil 31(4): 349–355. https://doi.org/10.3233/NRE-2012–00803Dordevic M, Hoelzer S, Russo A, García Alanis JC, Müller NG (2022) The Role of the Precuneus in Human Spatial Updating in a Real Environment Setting – A cTBS Study. Life 12(8): 1239. https://doi.org/10.3390/life12081239Ogawa T, Shimobayashi H, Hirayama JI, Kawanabe M (2022) Asymmetric directed functional connectivity within the frontoparietal motor network during motor imagery and execution. Neuroimage 247: 118794. https://doi.org/10.1016/j.neuroimage.2021.118794Araneda R, Dricot L, Ebner-Karestinos D, Paradis J, Gordon AM, Friel KM, Bleyenheuft Y (2021) Brain activation changes following motor training in children with unilateral cerebral palsy: An fMRI study. Ann Phys Rehabil Med 64(3): 101502. https://doi.org/10.1016/j.rehab.2021.101502Putzolu M, Samogin J, Cosentino C, Mezzarobba S, Bonassi G, Lagravinese G, Vato A, Mantini D, Avanzino L, Pelosin E (2022) Neural oscillations during motor imagery of complex gait: an HdEEG study. Sci Rep 12(1): 4314. https://doi.org/10.1038/s41598–022–07511-xPutzolu M, Samogin J, Bonassi G, Cosentino C, Mezzarobba S, Botta A, Avanzino L, Mantini D, Vato A, Pelosin E (2024) Motor imagery ability scores are related to cortical activation during gait imagery. Sci Rep 14(1): 5207. https://doi.org/10.1038/s41598–024–54966–1