Prefrontal and Vestibular Cortex Activation During Overground and Treadmill Walking

  • Brian SylcottEmail author
  • Mark Hinderaker
  • Mason Smith
  • John Willson
  • Chia-Cheng Lin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)


When walking on a treadmill, people can experience a sensory mismatch resulting from the lack of visual flow. In this study, functional Near‐Infrared Spectrometry (fNIRS) was used to investigate hemodynamic changes in the brain during overground and treadmill walking. Nine healthy right-handed subjects (25 ± 3 years) were recruited in this study. The test conditions included walking overground, on a split‐belt treadmill, and on a standard treadmill. Results showed significantly increased activity in the prefrontal cortex (PFC) and the temporoparietal junction (Vestibular Cortex-VEST) on both treadmills compared with overground walking. Walking on the standard treadmill significantly increased activation in the PFC and both the left and right VEST compared with the split‐belt treadmill. Our results suggest that walking on the treadmill provokes increased PFC and VEST activation. This finding may explain the fleeting sensation of dizziness after stopping walking on a treadmill.


Treadmill walking Sensory integration Functional near-infrared spectroscopy 


  1. 1.
    Reisman, D.S., et al.: Split-belt treadmill adaptation transfers to overground walking in persons poststroke. Neurorehabilitation Neural Repair 23(7), 735–744 (2009)
  2. 2.
    Reisman, D.S., Bastian, A.J., Morton, S.M.: Neurophysiologic and rehabilitation insights from the split-belt and other locomotor adaptation paradigms. Phys. Ther. 90(2), 187–195 (2010)
  3. 3.
    Valentín-Gudiol, M., et al.: Treadmill interventions in children under six years of age at risk of neuromotor delay. Cochrane Database Syst. Rev. 7(7), CD009242–CD009242 (2017)
  4. 4.
    Guzik, A., Drużbicki, M., Wolan-Nieroda, A.: Assessment of two gait training models: conventional physical therapy and treadmill exercise, in terms of their effectiveness after stroke. Hippokratia 22(2), 51–59 (2018)
  5. 5.
    Matsas, A., Taylor, N., McBurney, H.: Knee joint kinematics from familiarised treadmill walking can be generalised to overground walking in young unimpaired subjects. Gait Posture 11(1), 46–53 (2000)
  6. 6.
    Murray, M.P., et al.: Treadmill vs. floor walking: kinematics, electromyogram, and heart rate. J. Appl. Physiol. 59(1), 87–91 (1985)
  7. 7.
    Wall, J.C., Charteris, J.: A kinematic study of long-term habituation to treadmill walking. Ergonomics 24(7), 531–542 (1981)
  8. 8.
    Campos, J.L., Butler, J.S., Bulthoff, H.H.: Contributions of visual and proprioceptive information to travelled distance estimation during changing sensory congruencies. Exp. Brain Res. 232(10), 3277–3289 (2014)
  9. 9.
    Eikema, D.J.A., et al.: Optic flow improves adaptability of spatiotemporal characteristics during split-belt locomotor adaptation with tactile stimulation. Exp. Brain Res. 234(2), 511–522 (2016)
  10. 10.
    Labriffe, M., et al.: Brain activity during mental imagery of Gait versus Gait-like plantar stimulation: a novel combined functional MRI paradigm to better understand cerebral Gait control. Front. Hum. Neurosci. 11, 106–106 (2017)
  11. 11.
    Allali, G., et al.: The neural basis of age-related changes in motor imagery of Gait: an fMRI study. J. Gerontol. Ser. A 69(11), 1389–1398 (2013)
  12. 12.
    Kapreli, E., et al.: Lateralization of brain activity during lower limb joints movement. An fMRI study. NeuroImage 32(4), 1709–1721 (2006)
  13. 13.
    Scarapicchia, V., et al.: Functional magnetic resonance imaging and functional near-infrared spectroscopy: insights from combined recording studies. Front. Hum. Neurosci. 11, 419–419 (2017)
  14. 14.
    Yuan, Z., Ye, J.: Fusion of fNIRS and fMRI data: identifying when and where hemodynamic signals are changing in human brains. Front. Hum. Neurosci. 7, 676 (2013)
  15. 15.
    Perrey, S.: Possibilities for examining the neural control of gait in humans with fNIRS. Front. Physiol. 5, 204–204 (2014)
  16. 16.
    Santosa, H., et al.: The NIRS Brain AnalyzIR Toolbox. Algorithms 11(5), 73 (2018)
  17. 17.
    Abdelnour, A.F., Huppert, T.: Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model. Neuroimage 46(1), 133–143 (2009)
  18. 18.
    Huppert, T.J.: Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics 3(1), 010401 (2016)
  19. 19.
    Wilkinson, G.N., Rogers, C.E.: Symbolic description of factorial models for analysis of variance. J. Roy. Stat. Soc.: Ser. C Appl. Stat. 22(3), 392–399 (1973)
  20. 20.
    Thach, W.T., Bastian, A.J.: Role of the cerebellum in the control and adaptation of gait in health and disease. Prog. Brain Res. 143, 353–366 (2004)

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Brian Sylcott
    • 1
    Email author
  • Mark Hinderaker
    • 2
  • Mason Smith
    • 2
  • John Willson
    • 2
  • Chia-Cheng Lin
    • 2
  1. 1.Department of EngineeringEast Carolina UniversityGreenvilleUSA
  2. 2.Department of Physical TherapyEast Carolina UniversityGreenvilleUSA

Personalised recommendations