Advertisement

Brain–Machine Interface Induced Morpho-Functional Remodeling of the Neural Motor System in Severe Chronic Stroke

  • Andrea CariaEmail author
  • Josué Luiz Dalboni da Rocha
  • Giuseppe Gallitto
  • Niels Birbaumer
  • Ranganatha Sitaram
  • Ander Ramos Murguialday
Original Article
  • 10 Downloads

Abstract

Brain–machine interfaces (BMI) permit bypass motor system disruption by coupling contingent neuroelectric signals related to motor activity with prosthetic devices that enhance afferent and proprioceptive feedback to the somatosensory cortex. In this study, we investigated neural plasticity in the motor network of severely impaired chronic stroke patients after an EEG-BMI-based treatment reinforcing sensorimotor contingency of ipsilesional motor commands. Our structural connectivity analysis revealed decreased fractional anisotropy in the splenium and body of the corpus callosum, and in the contralesional hemisphere in the posterior limb of the internal capsule, the posterior thalamic radiation, and the superior corona radiata. Functional connectivity analysis showed decreased negative interhemispheric coupling between contralesional and ipsilesional sensorimotor regions, and decreased positive intrahemispheric coupling among contralesional sensorimotor regions. These findings indicate that BMI reinforcing ipsilesional brain activity and enhancing proprioceptive function of the affected hand elicits reorganization of contralesional and ipsilesional somatosensory and motor-assemblies as well as afferent and efferent connection–related motor circuits that support the partial re-establishment of the original neurophysiology of the motor system even in severe chronic stroke.

Key Words

Brain–machine interface stroke DTI functional connectivity motor recovery 

Notes

Acknowledgments

This work was supported by the German Federal Ministry of Education and Research (BMBF, Förderzeichen 01GQ0831); Deutsche Forschungsgemeinschaft (DFG) Koselleck (BI 195/58-1); European Research Council (ERC 227632); European Union FP7-ICT-2009-231724 - HUMOR: Human Behavioral Modeling for Enhancing Learning by Optimizing Human-Robot Interaction, FP7-ICT-2009-247935 – BETTER: BNCI-driven Robotic Physical Therapies in Stroke Rehabilitation of Gait Disorders; Italian Ministry of Health GR-2009-159190; BW-Stiftung (ROB-1); Werner Reichardt Centre for Integrative Neuroscience (CIN). The authors would like to thank Balint Varkuti, Leonhard Läer, Pavel Terekhin, Nicolas Lindau, and Bjorn Schiffler for their support.

Required Author Forms

Disclosure forms provided by the authors are available with the online version of this article.

Compliance with Ethical Standards

Informed consent, including informed consent to publish identifying information/images in an online open-access publication, was obtained from all patients involved. The study was approved by the ethics committee of the Faculty of Medicine of the University of Tübingen. The methods carried out in this work are in accordance with the approved guidelines and regulations.

Competing Interests

The authors declare that they have no competing interests.

Supplementary material

13311_2019_816_MOESM1_ESM.docx (3.6 mb)
ESM 1 (DOCX 3734 kb)
13311_2019_816_MOESM2_ESM.pdf (2.3 mb)
ESM 2 (PDF 2363 kb)

References

  1. 1.
    Hochberg LR, Bacher D, Jarosiewicz B et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398), 372–375 (2012).PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    Hochberg LR, Serruya MD, Friehs GM et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164–171 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  3. 3.
    Collinger JL, Wodlinger B, Downey JE et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet, 381(9866), 557–564 (2013).PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Buch E, Weber C, Cohen LG et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke, 39(3), 910–917 (2008).PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Bundy DT, Wronkiewicz M, Sharma M, Moran DW, Corbetta M, Leuthardt EC. Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors. J Neural Eng, 9(3), 036011 (2012).PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Ang KK, Guan C, Chua KS et al. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. Conf Proc IEEE Eng Med Biol Soc, 2010, 5549–5552 (2010).PubMedPubMedCentralGoogle Scholar
  7. 7.
    Shindo K, Kawashima K, Ushiba J et al. Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J Rehabil Med, 43(10), 951–957 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Young BM, Nigogosyan Z, Remsik A et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Front Neuroeng, 7, 25 (2014).PubMedPubMedCentralGoogle Scholar
  9. 9.
    Young BM, Nigogosyan Z, Walton LM et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng, 7, 26 (2014).PubMedPubMedCentralGoogle Scholar
  10. 10.
    Young BM, Stamm JM, Song J et al. Brain-Computer Interface Training after Stroke Affects Patterns of Brain-Behavior Relationships in Corticospinal Motor Fibers. Front Hum Neurosci, 10, 457 (2016).PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Birbaumer N, Cohen LG. Brain-computer interfaces: communication and restoration of movement in paralysis. J Physiol, 579(Pt 3), 621–636 (2007).PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Caria A, Weber C, Brotz D et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology, 48(4), 578–582 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  13. 13.
    Varkuti B, Guan C, Pan Y et al. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair, 27(1), 53–62 (2013).PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol, 7(11), 1032–1043 (2008).PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Ramos-Murguialday A, Broetz D, Rea M et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol, 74(1), 100–108 (2013).PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Krakauer JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol, 19(1), 84–90 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Pomeroy V, Aglioti SM, Mark VW et al. Neurological principles and rehabilitation of action disorders: rehabilitation interventions. Neurorehabil Neural Repair, 25(5 Suppl), 33S–43S (2011).PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Ward NS, Cohen LG. Mechanisms underlying recovery of motor function after stroke. Arch Neurol, 61(12), 1844–1848 (2004).PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Stinear CM, Barber PA, Smale PR, Coxon JP, Fleming MK, Byblow WD. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain, 130(Pt 1), 170–180 (2007).PubMedPubMedCentralGoogle Scholar
  20. 20.
    Liepert J, Hamzei F, Weiller C. Motor cortex disinhibition of the unaffected hemisphere after acute stroke. Muscle Nerve, 23(11), 1761–1763 (2000).PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Hummel FC, Cohen LG. Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke? Lancet Neurol, 5(8), 708–712 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Rehme AK, Eickhoff SB, Wang LE, Fink GR, Grefkes C. Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke. Neuroimage, 55(3), 1147–1158 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Murase N, Duque J, Mazzocchio R, Cohen LG. Influence of interhemispheric interactions on motor function in chronic stroke. Ann Neurol, 55(3), 400–409 (2004).PubMedCrossRefPubMedCentralGoogle Scholar
  24. 24.
    Carey JR, Kimberley TJ, Lewis SM et al. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain, 125(Pt 4), 773–788 (2002).PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Carey LM, Abbott DF, Egan GF, Bernhardt J, Donnan GA. Motor impairment and recovery in the upper limb after stroke: behavioral and neuroanatomical correlates. Stroke, 36(3), 625–629 (2005).PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Rossini PM, Dal Forno G. Neuronal post-stroke plasticity in the adult. Restor Neurol Neurosci, 22(3–5), 193–206 (2004).PubMedPubMedCentralGoogle Scholar
  27. 27.
    Ward NS, Brown MM, Thompson AJ, Frackowiak RS. Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain, 126(Pt 11), 2476–2496 (2003).PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Ward NS, Brown MM, Thompson AJ, Frackowiak RS. Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain, 126(Pt 6), 1430–1448 (2003).PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Loubinoux I, Carel C, Pariente J et al. Correlation between cerebral reorganization and motor recovery after subcortical infarcts. Neuroimage, 20(4), 2166–2180 (2003).PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Broetz D, Braun C, Weber C, Soekadar SR, Caria A, Birbaumer N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil Neural Repair, 24(7), 674–679 (2010).PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    Song J, Nair VA, Young BM et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci, 9, 195 (2015).PubMedPubMedCentralGoogle Scholar
  32. 32.
    Song J, Young BM, Nigogosyan Z et al. Characterizing relationships of DTI, fMRI, and motor recovery in stroke rehabilitation utilizing brain-computer interface technology. Front Neuroeng, 7, 31 (2014).PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Marins T, Rodrigues EC, Bortolini T, Melo B, Moll J, Tovar-Moll F. Structural and functional connectivity changes in response to short-term neurofeedback training with motor imagery. Neuroimage, 194, 283–290 (2019).PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Tyc F, Boyadjian A. Plasticity of motor cortex induced by coordination and training. Clin Neurophysiol, 122(1), 153–162 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Uswatte G, Taub E, Morris D, Vignolo M, McCulloch K. Reliability and validity of the upper-extremity Motor Activity Log-14 for measuring real-world arm use. Stroke, 36(11), 2493–2496 (2005).PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Hurn J, Kneebone I, Cropley M. Goal setting as an outcome measure: A systematic review. Clin Rehabil, 20(9), 756–772 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Platz T, Pinkowski C, van Wijck F, Kim IH, di Bella P, Johnson G. Reliability and validity of arm function assessment with standardized guidelines for the Fugl-Meyer Test, Action Research Arm Test and Box and Block Test: a multicentre study. Clin Rehabil, 19(4), 404–411 (2005).PubMedCrossRefGoogle Scholar
  38. 38.
    Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics, 4(3), 316–329 (2007).PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci, 4(6), 469–480 (2003).PubMedCrossRefGoogle Scholar
  40. 40.
    Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci, 15(4), 528–536 (2012).PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Puig J, Blasco G, Schlaug G et al. Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke. Neuroradiology, 59(4), 343–351 (2017).PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH, Jr. Age-related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Hum Brain Mapp, 31(3), 378–390 (2010).PubMedPubMedCentralGoogle Scholar
  43. 43.
    Winklewski PJ, Sabisz A, Naumczyk P, Jodzio K, Szurowska E, Szarmach A. Understanding the Physiopathology Behind Axial and Radial Diffusivity Changes-What Do We Know? Front Neurol, 9, 92 (2018).PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Song SK, Yoshino J, Le TQ et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage, 26(1), 132–140 (2005).PubMedCrossRefPubMedCentralGoogle Scholar
  45. 45.
    Mori S, Oishi K, Jiang H et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage, 40(2), 570–582 (2008).PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Smola AJ, Scholkopf B. A tutorial on support vector regression. Stat Comput, 14(3), 199–222 (2004).CrossRefGoogle Scholar
  47. 47.
    Yourganov G, Fridriksson J, Rorden C, Gleichgerrcht E, Bonilha L. Multivariate Connectome-Based Symptom Mapping in Post-Stroke Patients: Networks Supporting Language and Speech. J Neurosci, 36(25), 6668–6679 (2016).PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Cuingnet R, Rosso C, Chupin M et al. Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome. Med Image Anal, 15(5), 729–737 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Cuingnet R, Gerardin E, Tessieras J et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage, 56(2), 766–781 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  50. 50.
    Lao Z, Shen D, Xue Z, Karacali B, Resnick SM, Davatzikos C. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage, 21(1), 46–57 (2004).PubMedCrossRefPubMedCentralGoogle Scholar
  51. 51.
    Kloppel S, Stonnington CM, Chu C et al. Automatic classification of MR scans in Alzheimer’s disease. Brain, 131(Pt 3), 681–689 (2008).PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Vemuri P, Gunter JL, Senjem ML et al. Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage, 39(3), 1186–1197 (2008).PubMedCrossRefPubMedCentralGoogle Scholar
  53. 53.
    Kriegeskorte N, Goebel R, Bandettini P. Information-based functional brain mapping. Proc Natl Acad Sci U S A, 103(10), 3863–3868 (2006).PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Dalboni da Rocha JL, Coutinho G, Bramati I, Moll FT, Sitaram R. Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders. Brain Imaging Behav, 1–12.  https://doi.org/10.1007/s11682-018-0002-2 (2018).
  55. 55.
    Zurita M, Montalba C, Labbe T et al. Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data. NeuroImage Clin, 20, 724–730 (2018).PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Andersen SM, Rapcsak SZ, Beeson PM. Cost function masking during normalization of brains with focal lesions: still a necessity? Neuroimage, 53(1), 78–84 (2010).PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Lieberman MD, Cunningham WA. Type I and Type II error concerns in fMRI research: re-balancing the scale. Soc Cogn Affect Neurosci, 4(4), 423–428 (2009).PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Slotnick SD. Cluster success: fMRI inferences for spatial extent have acceptable false-positive rates. Cogn Neurosci, 8(3), 150–155 (2017).PubMedCrossRefPubMedCentralGoogle Scholar
  59. 59.
    Cunningham WA, Koscik TR. Balancing Type I and Type II error concerns in fMRI through compartmentalized analysis. Cogn Neurosci, 8(3), 147–149 (2017).PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 44(1), 83–98 (2009).PubMedCrossRefPubMedCentralGoogle Scholar
  61. 61.
    Spisak T, Spisak Z, Zunhammer M et al. Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power. Neuroimage, 185, 12–26 (2018).PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Wilke M, Lidzba K. LI-tool: a new toolbox to assess lateralization in functional MR-data. J Neurosci Methods, 163(1), 128–136 (2007).PubMedCrossRefPubMedCentralGoogle Scholar
  63. 63.
    Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2(3), 125–141 (2012).CrossRefGoogle Scholar
  64. 64.
    Grefkes C, Nowak DA, Eickhoff SB et al. Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol, 63(2), 236–246 (2008).PubMedCrossRefPubMedCentralGoogle Scholar
  65. 65.
    Grefkes C, Fink GR. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain, 134(Pt 5), 1264–1276 (2011).PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Palmer LM, Schulz JM, Murphy SC, Ledergerber D, Murayama M, Larkum ME. The cellular basis of GABA(B)-mediated interhemispheric inhibition. Science, 335(6071), 989–993 (2012).PubMedCrossRefPubMedCentralGoogle Scholar
  67. 67.
    Ramos-Murguialday A, Curado MR, Broetz D et al. Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up. Neurorehabil Neural Repair, 33(3), 188–198 (2019).PubMedCrossRefPubMedCentralGoogle Scholar
  68. 68.
    Hofer S, Frahm J. Topography of the human corpus callosum revisited--comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. Neuroimage, 32(3), 989–994 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  69. 69.
    Fling BW, Benson BL, Seidler RD. Transcallosal sensorimotor fiber tract structure-function relationships. Hum Brain Mapp, 34(2), 384–395 (2013).PubMedCrossRefPubMedCentralGoogle Scholar
  70. 70.
    Calautti C, Leroy F, Guincestre JY, Marie RM, Baron JC. Sequential activation brain mapping after subcortical stroke: changes in hemispheric balance and recovery. Neuroreport, 12(18), 3883–3886 (2001).PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    Marshall RS, Perera GM, Lazar RM, Krakauer JW, Constantine RC, DeLaPaz RL. Evolution of cortical activation during recovery from corticospinal tract infarction. Stroke, 31(3), 656–661 (2000).PubMedCrossRefPubMedCentralGoogle Scholar
  72. 72.
    Nishimura Y, Onoe H, Morichika Y, Perfiliev S, Tsukada H, Isa T. Time-dependent central compensatory mechanisms of finger dexterity after spinal cord injury. Science, 318(5853), 1150–1155 (2007).PubMedCrossRefPubMedCentralGoogle Scholar
  73. 73.
    Liu G, Dang C, Chen X et al. Structural remodeling of white matter in the contralesional hemisphere is correlated with early motor recovery in patients with subcortical infarction. Restor Neurol Neurosci, 33(3), 309–319 (2015).PubMedPubMedCentralGoogle Scholar
  74. 74.
    Schaechter JD, Fricker ZP, Perdue KL et al. Microstructural status of ipsilesional and contralesional corticospinal tract correlates with motor skill in chronic stroke patients. Hum Brain Mapp, 30(11), 3461–3474 (2009).PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Koch P, Schulz R, Hummel FC. Structural connectivity analyses in motor recovery research after stroke. Ann Clin Transl Neurol, 3(3), 233–244 (2016).PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Jang SH, Cho SH, Kim YH et al. Diffusion anisotrophy in the early stages of stroke can predict motor outcome. Restor Neurol Neurosci, 23(1), 11–17 (2005).PubMedPubMedCentralGoogle Scholar
  77. 77.
    Johansen-Berg H, Scholz J, Stagg CJ. Relevance of structural brain connectivity to learning and recovery from stroke. Front Syst Neurosci, 4, 146 (2010).PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Taubert M, Draganski B, Anwander A et al. Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J Neurosci, 30(35), 11670–11677 (2010).PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Beaulieu C. Chapter 8 - The Biological Basis of Diffusion Anisotropy. In: Diffusion MRI (2nd Edition). (Academic Press, San Diego, 2014) 155–183.CrossRefGoogle Scholar
  80. 80.
    Wilkins KB, Owen M, Ingo C, Carmona C, Dewald JPA, Yao J. Neural Plasticity in Moderate to Severe Chronic Stroke Following a Device-Assisted Task-Specific Arm/Hand Intervention. Front Neurol, 8, 284 (2017).PubMedPubMedCentralCrossRefGoogle Scholar
  81. 81.
    Granziera C, Ay H, Koniak SP, Krueger G, Sorensen AG. Diffusion tensor imaging shows structural remodeling of stroke mirror region: results from a pilot study. Eur Neurol, 67(6), 370–376 (2012).PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Wan CY, Zheng X, Marchina S, Norton A, Schlaug G. Intensive therapy induces contralateral white matter changes in chronic stroke patients with Broca’s aphasia. Brain Lang, 136, 1–7 (2014).PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Li Z, Li C, Fan L et al. Altered microstructure rather than morphology in the corpus callosum after lower limb amputation. Sci Rep, 7, 44780 (2017).PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Jang SH. Contra-lesional somatosensory cortex activity and somatosensory recovery in two stroke patients. J Rehabil Med, 43(3), 268–270 (2011).PubMedCrossRefPubMedCentralGoogle Scholar
  85. 85.
    Rehme AK, Eickhoff SB, Rottschy C, Fink GR, Grefkes C. Activation likelihood estimation meta-analysis of motor-related neural activity after stroke. Neuroimage, 59(3), 2771–2782 (2012).PubMedCrossRefPubMedCentralGoogle Scholar
  86. 86.
    Grefkes C, Ward NS. Cortical reorganization after stroke: how much and how functional? Neuroscientist, 20(1), 56–70 (2014).PubMedCrossRefPubMedCentralGoogle Scholar
  87. 87.
    Di Pino G, Pellegrino G, Assenza G et al. Modulation of brain plasticity in stroke: a novel model for neurorehabilitation. Nat Rev Neurol, 10(10), 597–608 (2014).PubMedCrossRefPubMedCentralGoogle Scholar
  88. 88.
    Johansen-Berg H, Rushworth MF, Bogdanovic MD, Kischka U, Wimalaratna S, Matthews PM. The role of ipsilateral premotor cortex in hand movement after stroke. Proc Natl Acad Sci U S A, 99(22), 14518–14523 (2002).PubMedPubMedCentralCrossRefGoogle Scholar
  89. 89.
    Schaechter JD, Perdue KL. Enhanced cortical activation in the contralesional hemisphere of chronic stroke patients in response to motor skill challenge. Cereb Cortex, 18(3), 638–647 (2008).PubMedCrossRefPubMedCentralGoogle Scholar
  90. 90.
    Eyre JA. Corticospinal tract development and its plasticity after perinatal injury. Neurosci Biobehav Rev, 31(8), 1136–1149 (2007).PubMedCrossRefPubMedCentralGoogle Scholar
  91. 91.
    Fregni F, Pascual-Leone A. Hand motor recovery after stroke: tuning the orchestra to improve hand motor function. Cogn Behav Neurol, 19(1), 21–33 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  92. 92.
    Nowak DA, Grefkes C, Dafotakis M et al. Effects of low-frequency repetitive transcranial magnetic stimulation of the contralesional primary motor cortex on movement kinematics and neural activity in subcortical stroke. Arch Neurol, 65(6), 741–747 (2008).PubMedCrossRefPubMedCentralGoogle Scholar
  93. 93.
    Schwerin S, Dewald JP, Haztl M, Jovanovich S, Nickeas M, MacKinnon C. Ipsilateral versus contralateral cortical motor projections to a shoulder adductor in chronic hemiparetic stroke: implications for the expression of arm synergies. Exp Brain Res, 185(3), 509–519 (2008).PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Ward NS, Newton JM, Swayne OB et al. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain, 129(Pt 3), 809–819 (2006).PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Daskalakis ZJ, Christensen BK, Fitzgerald PB, Roshan L, Chen R. The mechanisms of interhemispheric inhibition in the human motor cortex. J Physiol, 543(Pt 1), 317–326 (2002).PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Duque J, Murase N, Celnik P et al. Intermanual Differences in movement-related interhemispheric inhibition. J Cogn Neurosci, 19(2), 204–213 (2007).PubMedCrossRefPubMedCentralGoogle Scholar
  97. 97.
    Duque J, Hummel F, Celnik P, Murase N, Mazzocchio R, Cohen LG. Transcallosal inhibition in chronic subcortical stroke. Neuroimage, 28(4), 940–946 (2005).PubMedCrossRefPubMedCentralGoogle Scholar
  98. 98.
    Avanzino L, Bassolino M, Pozzo T, Bove M. Use-dependent hemispheric balance. J Neurosci, 31(9), 3423–3428 (2011).PubMedPubMedCentralCrossRefGoogle Scholar
  99. 99.
    Avanzino L, Teo JT, Rothwell JC. Intracortical circuits modulate transcallosal inhibition in humans. J Physiol, 583(Pt 1), 99–114 (2007).PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Wahl M, Lauterbach-Soon B, Hattingen E et al. Human motor corpus callosum: topography, somatotopy, and link between microstructure and function. J Neurosci, 27(45), 12132–12138 (2007).PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Serrien DJ, Strens LH, Oliviero A, Brown P. Repetitive transcranial magnetic stimulation of the supplementary motor area (SMA) degrades bimanual movement control in humans. Neurosci Lett, 328(2), 89–92 (2002).PubMedCrossRefPubMedCentralGoogle Scholar
  102. 102.
    Giovannelli F, Borgheresi A, Balestrieri F et al. Role of the right dorsal premotor cortex in “physiological” mirror EMG activity Exp Brain Res, 175(4), 633–640 (2006).PubMedCrossRefPubMedCentralGoogle Scholar
  103. 103.
    van den Berg FE, Swinnen SP, Wenderoth N. Hemispheric asymmetries of the premotor cortex are task specific as revealed by disruptive TMS during bimanual versus unimanual movements. Cereb Cortex, 20(12), 2842–2851 (2010).PubMedCrossRefPubMedCentralGoogle Scholar
  104. 104.
    Ni Z, Gunraj C, Nelson AJ et al. Two phases of interhemispheric inhibition between motor related cortical areas and the primary motor cortex in human. Cereb Cortex, 19(7), 1654–1665 (2009).PubMedCrossRefPubMedCentralGoogle Scholar
  105. 105.
    Daskalakis ZJ, Paradiso GO, Christensen BK, Fitzgerald PB, Gunraj C, Chen R. Exploring the connectivity between the cerebellum and motor cortex in humans. J Physiol, 557(Pt 2), 689–700 (2004).PubMedCrossRefPubMedCentralGoogle Scholar
  106. 106.
    Ejaz N, Xu J, Branscheidt M et al. Evidence for a subcortical origin of mirror movements after stroke: a longitudinal study. Brain, 141(3), 837–847 (2018).PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Calautti C, Jones PS, Naccarato M et al. Further evidence for a non-cortical origin of mirror movements after stroke. Brain, 142(1), e1 (2019).PubMedCrossRefPubMedCentralGoogle Scholar
  108. 108.
    Lam TK, Dawson DR, Honjo K et al. Neural coupling between contralesional motor and frontoparietal networks correlates with motor ability in individuals with chronic stroke. J Neurol Sci, 384, 21–29 (2018).PubMedCrossRefPubMedCentralGoogle Scholar
  109. 109.
    Ludemann-Podubecka J, Bosl K, Nowak DA. Inhibition of the contralesional dorsal premotor cortex improves motor function of the affected hand following stroke. Eur J Neurol, 23(4), 823–830 (2016).PubMedCrossRefPubMedCentralGoogle Scholar
  110. 110.
    Celnik P. Understanding and modulating motor learning with cerebellar stimulation. Cerebellum, 14(2), 171–174 (2015).PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Spampinato DA, Block HJ, Celnik PA. Cerebellar-M1 Connectivity Changes Associated with Motor Learning Are Somatotopic Specific. J Neurosci, 37(9), 2377–2386 (2017).PubMedPubMedCentralCrossRefGoogle Scholar
  112. 112.
    Wahl M, Lauterbach-Soon B, Hattingen E, Hubers A, Ziemann U. Callosal anatomical and effective connectivity between primary motor cortices predicts visually cued bimanual temporal coordination performance. Brain Struct Funct, 221(7), 3427–3443 (2016).PubMedCrossRefPubMedCentralGoogle Scholar
  113. 113.
    Koerte I, Heinen F, Fuchs T et al. Anisotropy of callosal motor fibers in combination with transcranial magnetic stimulation in the course of motor development. Investig Radiol, 44(5), 279–284 (2009).CrossRefGoogle Scholar
  114. 114.
    Takechi U, Matsunaga K, Nakanishi R et al. Longitudinal changes of motor cortical excitability and transcallosal inhibition after subcortical stroke. Clin Neurophysiol, 125(10), 2055–2069 (2014).PubMedCrossRefPubMedCentralGoogle Scholar
  115. 115.
    Zaaimi B, Edgley SA, Soteropoulos DS, Baker SN. Changes in descending motor pathway connectivity after corticospinal tract lesion in macaque monkey. Brain, 135(Pt 7), 2277–2289 (2012).PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© The American Society for Experimental NeuroTherapeutics, Inc. 2019

Authors and Affiliations

  1. 1.Department of Psychology and Cognitive SciencesUniversity of TrentoRoveretoItaly
  2. 2.Istituto di Ricovero e Cura a Carattere ScientificoFondazione Ospedale San CamilloVeniceItaly
  3. 3.Institut für Medizinische Psychologie und VerhaltensneurobiologieUniversität TübingenTübingenGermany
  4. 4.Brain and Language Laboratory, Department of Clinical NeuroscienceUniversity of GenevaGenevaSwitzerland
  5. 5.Institute of Biological and Medical EngineeringPontificia Universidad Católica de ChileSantiagoChile
  6. 6.Department of Psychiatry, Section of Neuroscience, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
  7. 7.Laboratory for Brain-Machine Interfaces and Neuromodulation, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
  8. 8.Health Technologies DepartmentTECNALIASan SebastianSpain

Personalised recommendations