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The Antagonistic Alterations of Cerebellar Functional Segregation and Integration in Athletes with Fast Demands of Visual-Motor Coordination

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Abstract

The theoretical foundation of brain-computer interface partly lies in the neural mechanism of motor function plasticity. There has been extensive research on the functional neuroplasticity induced by motor skill training in the human cerebral cortex; however, less is known about the specifics within the cerebellum. The present study employed resting-state functional magnetic resonance imaging (fMRI) data from athletes and matched non-athlete controls to investigate the adaptation of cerebellar functional segregation and integration in athletes who require rapid visual-motor coordination. First, this study utilized a data-driven blind-deconvolution hemodynamic response functions (HRF) retrieval technique to estimate voxel-wise HRF that represent local functional segregation. Second, the study quantified effective connectivity using conditional Granger causality (CGC) analysis as a means of characterizing directed functional integration. Lastly, the logistic regression classification model was applied to evaluating the importance of those significant features in two groups’ comparison. The athletes exhibited greater HRF response heights in the visual-spatial cognitive regions, but lower excitatory/inhibitory effects between these regions and the motor execution areas in the cerebellum when compared to the control group. These findings suggested that there was improved local functional segregation within the visual-spatial cognitive regions, as well as reduced functional integration between these regions and the motor execution areas in the cerebellum among athletes. Our results suggested the antagonistic alterations of cerebellar functional segregation and integration induced by motor skill training, and consequently to accelerate the reaction, movement planning, and execution in athletes who required fast demands of visual-motor coordination. Our findings shed new light on how motor skill training drove neuroplasticity within the cerebellum and offered a deeper understanding of the complementary hypotheses of neural efficiency and neural proficiency that underlay optimal athletic performance.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Gomez-Pilar J, Corralejo R, Nicolas-Alonso LF, Álvarez D, Hornero R. Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med Biol Eng Compu. 2016;54(11):1655–66.

    Google Scholar 

  2. Gomez-Pilar J, Corralejo R, Nicolas-Alonso LF, Álvarez D, Hornero R. Assessment of neurofeedback training by means of motor imagery based-BCI for cognitive rehabilitation. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. 2014;2014:3630–3.

    Google Scholar 

  3. Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain-computer interface paradigms. J Neural Eng. 2019;16(1): 011001.

    Google Scholar 

  4. Mridha MF, Das SC, Kabir MM, Lima AA, Islam MR, Watanobe Y. Brain-computer interface: advancement and challenges. Sensors (Basel, Switzerland). 2021;21(17).

  5. Gao Q, Yu Y, Su X, Tao Z, Zhang M, Wang Y, et al. Adaptation of brain functional stream architecture in athletes with fast demands of sensorimotor integration. Hum Brain Mapp. 2019;40(2):420–31.

    Google Scholar 

  6. Tao Z, Gao Q, Yu Y, Chen H. A study of brain plasticity of small-ball players based on ReHo method. Journal of Chengdu Sport University. 2017;43(06):98–102.

    Google Scholar 

  7. Shao M, Lin H, Yin D, Li Y, Wang Y, Ma J, et al. Learning to play badminton altered resting-state activity and functional connectivity of the cerebellar sub-regions in adults. PLoS ONE. 2019;14(10): e0223234.

    Google Scholar 

  8. Pezoulas VC, Michalopoulos K, Klados MA, Micheloyannis S, Bourbakis NG, Zervakis M. Functional connectivity analysis of cerebellum using spatially constrained spectral clustering. IEEE J Biomed Health Inform. 2019;23(4):1710–9.

    Google Scholar 

  9. Sereno MI, Diedrichsen J, Tachrount M, Testa-Silva G, d’Arceuil H, De Zeeuw C. The human cerebellum has almost 80% of the surface area of the neocortex. Proc Natl Acad Sci USA. 2020;117(32):19538–43.

    Google Scholar 

  10. Balsters JH, Cussans E, Diedrichsen J, Phillips KA, Preuss TM, Rilling JK, et al. Evolution of the cerebellar cortex: the selective expansion of prefrontal-projecting cerebellar lobules. Neuroimage. 2010;49(3):2045–52.

    Google Scholar 

  11. Barton RA, Venditti C. Rapid evolution of the cerebellum in humans and other great apes. Curr Biol. 2014;24(20):2440–4.

    Google Scholar 

  12. Kochiyama T, Ogihara N, Tanabe HC, Kondo O, Amano H, Hasegawa K, et al. Reconstructing the Neanderthal brain using computational anatomy. Sci Rep. 2018;8(1):6296.

    Google Scholar 

  13. Di X, Zhu S, Jin H, Wang P, Ye Z, Zhou K, et al. Altered resting brain function and structure in professional badminton players. Brain connectivity. 2012;2(4):225–33.

    Google Scholar 

  14. Li W, Kong X, Ma J. Effects of combat sports on cerebellar function in adolescents: a resting-state fMRI study. The British journal of radiology. 2021:20210826.

  15. Friston KJ, editor Introduction experimental design and statistical parametric mapping 2003.

  16. Friston KJ, Frith CD, Frackowiak RSJ. Time-dependent changes in effective connectivity measured with PET. 1993;1(1):69–79.

    Google Scholar 

  17. Geweke JF. Measures of conditional linear dependence and feedback between time series. J Am Stat Assoc. 1984;79(388):907–15.

    MathSciNet  MATH  Google Scholar 

  18. Ding M, Chen Y, Bressler SL. Granger causality: basic theory and application to neuroscience. Handbook of Time Series Analysis. 2006. p. 437–60.

  19. Deshpande G, LaConte S, James GA, Peltier S, Hu X. Multivariate Granger causality analysis of fMRI data. Hum Brain Mapp. 2009;30(4):1361–73.

    Google Scholar 

  20. Jiao Q, Lu G, Zhang Z, Zhong Y, Wang Z, Guo Y, et al. Granger causal influence predicts BOLD activity levels in the default mode network. 2011;32(1):154–61.

    Google Scholar 

  21. Gao Q, Duan X, Chen H. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality. Neuroimage. 2011;54(2):1280–8.

    Google Scholar 

  22. Gao Q, Zou K, He Z, Sun X, Chen H. Causal connectivity alterations of cortical-subcortical circuit anchored on reduced hemodynamic response brain regions in first-episode drug-naïve major depressive disorder. Sci Rep. 2016;6:21861.

    Google Scholar 

  23. Wu GR, Liao W, Stramaglia S, Ding JR, Chen HF, Marinazzo D. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med Image Anal. 2013;17(3):365–74.

    Google Scholar 

  24. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001;412(6843):150–7.

    Google Scholar 

  25. Lindquist MA, Wager TD. Validity and power in hemodynamic response modeling: a comparison study and a new approach. Hum Brain Mapp. 2007;28(8):764–84.

    Google Scholar 

  26. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9(1):97–113.

    Google Scholar 

  27. Malekpour S, Sethares WA. Conditional Granger causality and partitioned Granger causality: differences and similarities. Biol Cybern. 2015;109(6):627–37.

    MathSciNet  MATH  Google Scholar 

  28. Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19(4):1273–302.

    Google Scholar 

  29. Ji J, Liu J, Liang P, Zhang A. Learning effective connectivity network structure from fMRI data based on artificial immune algorithm. PLoS ONE. 2016;11(4): e0152600.

    Google Scholar 

  30. Handwerker DA, Ollinger JM, D’Esposito M. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage. 2004;21(4):1639–51.

    Google Scholar 

  31. Wu GR, Marinazzo D. Retrieving the hemodynamic response function in resting state fMRI: methodology and applications. PeerJ PrePrints. 2015;3:6050–3.

    Google Scholar 

  32. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(5):2322–45.

    Google Scholar 

  33. Colcombe SJ, Kramer AF, Erickson KI, Scalf P, McAuley E, Cohen NJ, et al. Cardiovascular fitness, cortical plasticity, and aging. Proc Natl Acad Sci USA. 2004;101(9):3316–21.

    Google Scholar 

  34. Talukdar T, Nikolaidis A, Zwilling CE, Paul EJ, Hillman CH, Cohen NJ, et al. Aerobic fitness explains individual differences in the functional brain connectome of healthy young adults. Cerebral cortex (New York, NY : 1991). 2018;28(10):3600–9.

  35. King M, Hernandez-Castillo CR, Poldrack RA, Ivry RB, Diedrichsen J. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nat Neurosci. 2019;22(8):1371–8.

    Google Scholar 

  36. Schmahmann JD. The cerebellum and cognition. Neurosci Lett. 2019;688:62–75.

    Google Scholar 

  37. Stoodley CJ, Valera EM, Schmahmann JD. An fMRI study of intra-individual functional topography in the human cerebellum. Behav Neurol. 2010;23(1–2):65–79.

    Google Scholar 

  38. Stoodley CJ, Valera EM, Schmahmann JD. Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study. Neuroimage. 2012;59(2):1560–70.

    Google Scholar 

  39. Schmahmann JD, Guell X, Stoodley CJ, Halko MA. The theory and neuroscience of cerebellar cognition. Annu Rev Neurosci. 2019;42:337–64.

    Google Scholar 

  40. Zang Z, Yan C, Dong Z, Huang J, Zang Y. Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing. J Neurosci Methods. 2012;203(2):418–26.

    Google Scholar 

  41. Guo Z, Li A, Yu L. “Neural efficiency” of athletes’ brain during visuo-spatial task: an fMRI study on table tennis players. Front Behav Neurosci. 2017;11:72.

    Google Scholar 

  42. Wolf S, Brölz E, Scholz D, Ramos-Murguialday A, Keune PM, Hautzinger M, et al. Winning the game: brain processes in expert, young elite and amateur table tennis players. Front Behav Neurosci. 2014;8:370.

    Google Scholar 

  43. Li L, Smith DM. Neural efficiency in athletes: a systematic review. Front Behav Neurosci. 2021;15: 698555.

    Google Scholar 

  44. Dietrich A. Transient hypofrontality as a mechanism for the psychological effects of exercise. Psychiatry Res. 2006;145(1):79–83.

    MathSciNet  Google Scholar 

  45. Filho E, Dobersek U, Husselman TA. The role of neural efficiency, transient hypofrontality and neural proficiency in optimal performance in self-paced sports: a meta-analytic review. Exp Brain Res. 2021;239(5):1381–93.

    Google Scholar 

  46. Holmes PS, Wright DJ. Motor cognition and neuroscience in sport psychology. Curr Opin Psychol. 2017;16:43–7.

    Google Scholar 

  47. Bertollo M, Doppelmayr M, Robazza C. Using brain technologies in practice. Handbook Sport Psychol. 2020;666–93.

Download references

Funding

This study was funded by the Ministry of Science and Technology of China (2021ZD0201701 and 2018AAA0100705), the National Natural Science Foundation of China (62173070, 82121003, 62036003, U1808204, 82072006, and 61906034), and the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD-D-202003).

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Authors and Affiliations

  1. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Mathematical Sciences, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People’s Republic of China

    Weiqi Zhou, Yan Li, Jie Li, Mengli Sun, Lisha Gong, Jiali Yu, Jinsong Leng, Fengmei Lu & Qing Gao

  2. School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Jueyan Wu

  3. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, High-Field, Chengdu, 611731, People’s Republic of China

    Rong Li & Huafu Chen

  4. The Third Department of Physical Education and Training, Chengdu Sport University, Chengdu, 610041, People’s Republic of China

    Chengbo Yang

  5. Information Technology Center, Chengdu Sport University, Chengdu, 610041, People’s Republic of China

    Mu Zhang

  6. Department of Neurology, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China

    Qin Chen

  7. The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Huafu Chen

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  1. Weiqi Zhou
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  9. Lisha Gong
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  13. Fengmei Lu
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  14. Huafu Chen
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  15. Qing Gao
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Corresponding authors

Correspondence to Fengmei Lu, Huafu Chen or Qing Gao.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the research ethical committee of School of Life Science and Technology, University of Electronic Science and Technology of China and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Zhou, W., Wu, J., Li, Y. et al. The Antagonistic Alterations of Cerebellar Functional Segregation and Integration in Athletes with Fast Demands of Visual-Motor Coordination. Cogn Comput 15, 1813–1824 (2023). https://doi.org/10.1007/s12559-023-10150-7

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