Motor Imagery Training During Arm Immobilization Prevents Corticomotor Idling: An EEG Resting-State Analysis

Brain Topography - Tập 33 - Trang 327-335 - 2020
Ursula Debarnot1,2, Franck Di Rienzo2, Sebastien Daligault3, Sophie Schwartz1
1Department of Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
2Inter-University Laboratory of Human Movement Biology-EA 7424, Villeurbanne, France
3Department of MEG, CERMEP Imagerie du Vivant, Bron, France

Tóm tắt

Limb disuse causes overt, measurable alterations in motor functions. Motor imagery (MI) practice has been used as a behavioral strategy to prevent motor impairments due to limb disuse or immobilization. Yet, how MI operates at the neural level in the context of short-term limb immobilization remains understudied. We hypothesized that MI treatment applied during 12 h of arm immobilization prevents immobilization-related changes in resting-state electroencephalographic (rsEEG) power and functional connectivity. Fourteen participants first underwent rsEEG after 12 h of normal motor activity (without immobilization). Then, rsEEG recording was performed after 12 h of arm immobilization either with MI treatment or without, each condition separated by 1 week, according to a randomized within-subjects design. MI treatment consisted in performing varied visual and kinaesthetic MI exercises (5 sessions of 15 min every two hours). The results showed that in the delta, theta, alpha and beta frequency bands, interhemispheric difference in sensors power over the motor cortex (i.e. C3 vs. C4) was reduced after arm immobilization, while it did not change when MI treatment was delivered during the immobilization period. Moreover, functional connectivity across the sensors-network in the delta (1–4 Hz) and alpha (8–12 Hz) frequency bands decreased after immobilization while it was restored by MI treatment. To conclude, MI counteracts functional neural changes within and between motor regions in the context of limb immobilization. Practical applications for motor rehabilitation strategies, particularly in stroke patients, are also discussed.

Tài liệu tham khảo

Ainsworth BE et al (2000) Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 32:S498–504 Avanzino L, Bassolino M, Pozzo T, Bove M (2011) Use-dependent hemispheric balance. J Neurosci 31:3423–3428. https://doi.org/10.1523/jneurosci.4893-10.2011 Avanzino L, Pelosin E, Abbruzzese G, Bassolino M, Pozzo T, Bove M (2014) Shaping motor cortex plasticity through proprioception. Cereb Cortex 24:2807–2814. https://doi.org/10.1093/cercor/bht139 Bassolino M, Campanella M, Bove M, Pozzo T, Fadiga L (2014) Training the motor cortex by observing the actions of others during immobilization. Cereb Cortex 24:3268–3276. https://doi.org/10.1093/cercor/bht190 Brauns I et al (2014) Changes in the theta band coherence during motor task after hand immobilization. Int Arch Med 7:51. https://doi.org/10.1186/1755-7682-7-51 Bretz F, Maurer W, Brannath W, Posch M (2009) A graphical approach to sequentially rejective multiple test procedures. Stat Med 28:586–604. https://doi.org/10.1002/sim.3495 Bretz F, Hothorn T, Westfall P (2010) Multiple comparisons using R. CRC Press, Boca Raton Carrillo-de-la-Pena MT, Lastra-Barreira C, Galdo-Alvarez S (2006) Limb (hand vs. foot) and response conflict have similar effects on event-related potentials (ERPs) recorded during motor imagery and overt execution. Eur J Neurosci 24:635–643. https://doi.org/10.1111/j.1460-9568.2006.04926.x Carrillo-de-la-Pena MT, Galdo-Alvarez S, Lastra-Barreira C (2008) Equivalent is not equal: primary motor cortex (MI) activation during motor imagery and execution of sequential movements. Brain Res 1226:134–143. https://doi.org/10.1016/j.brainres.2008.05.089 Champely S, Ekstrom C, Dalgaard P, Gill J, Weibelzahl S, Anandkumar A, Ford C, Volcic R, De Rosario H (2018) Power Functions for power analysis, version 1.2-2. R Package. pp 1–22 Core Team R (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Crews RT, Kamen G (2006) Motor-evoked potentials following imagery and limb disuse. Int J Neurosci 116:639–651. https://doi.org/10.1080/00207450600592198 Debarnot U, Huber C, Guillot A, Schwartz S (2018) Sensorimotor representation and functional motor changes following short-term arm immobilization. Behav Neurosci 132:595–603. https://doi.org/10.1037/bne0000274 Dickstein R, Deutsch JE (2007) Motor imagery in physical therapist practice. Phys Ther 87:942–953. https://doi.org/10.2522/ptj.20060331 Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL, Petersen SE (2008) A dual-networks architecture of top-down control. Trends Cognitive Sci 12:99–105. https://doi.org/10.1016/j.tics.2008.01.001 Edwards LJ, Muller KE, Wolfinger RD, Qaqish BF, Schabenberger O (2008) An R2 statistic for fixed effects in the linear mixed model. Stat Med 27:6137–6157. https://doi.org/10.1002/sim.3429 Fortuna M et al (2013) Cortical reorganization after hand immobilization: the beta qEEG spectral coherence evidences. PLoS One 8:e79912. https://doi.org/10.1371/journal.pone.0079912 Furlan L, Conforto AB, Cohen LG, Sterr A (2016) Upper limb immobilisation: a neural plasticity model with relevance to poststroke motor rehabilitation. Neural Plast 2016:8176217. https://doi.org/10.1155/2016/8176217 Galdo-Alvarez S, Bonilla FM, Gonzalez-Villar AJ, Carrillo-de-la-Pena MT (2016) Functional equivalence of imagined vs real performance of an inhibitory task: an EEG/ERP study. Front Hum Neurosci 10:467. https://doi.org/10.3389/fnhum.2016.00467 Hamedi M, Salleh ShH, Noor AM (2016) Electroencephalographic motor imagery brain connectivity analysis for BCI: a review. Neural Comput 28:999–1041. https://doi.org/10.1162/NECO_a_00838 Hetu S, Gregoire M, Saimpont A, Coll MP, Eugene F, Michon PE, Jackson PL (2013) The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav Rev 37:930–949. https://doi.org/10.1016/j.neubiorev.2013.03.017 Hoddes E, Dement WC, Zarcone V (1972) The development and use of the Stanford sleepiness scale. Psychophysiology 9:150 Huber R, Ghilardi MF, Massimini M, Ferrarelli F, Riedner BA, Peterson MJ, Tononi G (2006) Arm immobilization causes cortical plastic changes and locally decreases sleep slow wave activity. Nat Neurosci 9:1169–1176. https://doi.org/10.1038/nn1758 Jaeger BC, Edwards LJ, Das K, Sen PK (2017) An R2 statistic for fixed effects in the generalized linear mixed model. J Appl Stat 44:1086–1105 Jeannerod M, Decety J (1995) Mental motor imagery: a window into the representational stages of action. Curr Opin Neurobiol 5:727–732 Kleinbaum DG, Kupper LL, Muller KE (1988) Applied regression analysis and other multivariable methods. Nelson Education, Boston Machado D et al (2016a) Involvement of beta absolute power in motor areas after hand immobilization: an EEG study. MedicalExpress. https://doi.org/10.5935/MedicalExpress.2016.05.03 Machado D et al (2016b) Gamma absolute power reveals activation of motor areas after hand immobilization. MedicalExpress. https://doi.org/10.5935/MedicalExpress.2016.05.04 Malouin F, Jackson PL, Richards CL (2013) Towards the integration of mental practice in rehabilitation programs A critical review. Front Hum Neurosci 7:576. https://doi.org/10.3389/fnhum.2013.00576 Manaia F et al (2013) Does immobilization of dependent hand promote adaptative changes in cerebral cortex? An analysis through qEEG asymmetry. Neurosci Lett 538:20–25. https://doi.org/10.1016/j.neulet.2012.12.030 Marek S, Dosenbach NUF (2018) The frontoparietal network: function, electrophysiology, and importance of individual precision mapping. Dialogue Clin Neurosci 20:133–140 Mark VW, Taub E, Morris DM (2006) Neuroplasticity and constraint-induced movement therapy. Eur Medicophysica 42:269–284 Meugnot A, Agbangla NF, Almecija Y, Toussaint L (2015) Motor imagery practice may compensate for the slowdown of sensorimotor processes induced by short-term upper-limb immobilization. Psychol Res 79:489–499. https://doi.org/10.1007/s00426-014-0577-1 Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113 Pichiorri F et al (2015) Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol 77:851–865. https://doi.org/10.1002/ana.24390 Pinheiro J, Bornkamp B, Glimm E, Bretz F (2014) Model-based dose finding under model uncertainty using general parametric models. Stat Med 33:1646–1661. https://doi.org/10.1002/sim.6052 Pool EM, Rehme AK, Eickhoff SB, Fink GR, Grefkes C (2015) Functional resting-state connectivity of the human motor network: differences between right- and left-handers. Neuroimage 109:298–306. https://doi.org/10.1016/j.neuroimage.2015.01.034 Di Rienzo F, Collet C, Hoyek N, Guillot A (2014) Impact of neurologic deficits on motor imagery: a systematic review of clinical evaluations. Neuropsychol Rev 24:116–147. https://doi.org/10.1007/s11065-014-9257-6 Schuster C et al (2011) Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines. BMC Med 9:75. https://doi.org/10.1186/1741-7015-9-75 Sharma N, Pomeroy VM, Baron JC (2006) Motor imagery: a backdoor to the motor system after stroke? Stroke 37:1941–1952. https://doi.org/10.1161/01.STR.0000226902.43357.fc Stenekes MW, Geertzen JH, Nicolai JP, De Jong BM, Mulder T (2009) Effects of motor imagery on hand function during immobilization after flexor tendon repair. Arch Phys Med Rehabil 90:553–559. https://doi.org/10.1016/j.apmr.2008.10.029 Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011:879716. https://doi.org/10.1155/2011/879716 Takeuchi N, Izumi S (2012) Maladaptive plasticity for motor recovery after stroke: mechanisms and approaches. Neural Plast 2012:359728. https://doi.org/10.1155/2012/359728 Taub E, Uswatte G, Mark VW, Morris DM (2006) The learned nonuse phenomenon: implications for rehabilitation. Eur Medicophysica 42:241–256 Viaro R, Budri M, Parmiani P, Franchi G (2014) Adaptive changes in the motor cortex during and after longterm forelimb immobilization in adult rats. J Physiol 592:2137–2152. https://doi.org/10.1113/jphysiol.2013.268821 Wang H, Sun L (2018) The plasticity of resting-state brain networks associated with motor imagery training in chronic stroke patients. Ann Phys Rehabil Med 61:e20 Ward NS (2004) Functional reorganization of the cerebral motor system after stroke. Curr Opin Neurol 17:725–730 Williams SE, Cumming J, Ntoumanis N, Nordin-Bates SM, Ramsey R, Hall C (2012) Further validation and development of the movement imagery questionnaire. J Sport Exerc Psychol 34:621–646 Zhang H, Long Z, Ge R, Xu L, Jin Z, Yao L, Liu Y (2014) Motor imagery learning modulates functional connectivity of multiple brain systems in resting state. PLoS One 9:e85489. https://doi.org/10.1371/journal.pone.0085489