Applications of Near-Infrared Spectroscopy in Movement Disorders

  • Masahito MiharaEmail author
  • Noriaki HattoriEmail author
  • Ichiro MiyaiEmail author
Part of the Current Clinical Neurology book series (CCNEU, volume 44)


Near-infrared spectroscopy (NIRS) is a unique neuroimaging tool that allows for monitoring of cortical activation during daily activities such as standing, walking, and reaching. NIRS uses near-infrared light that penetrates skin and skull bone to measure task-related cortical vascular responses. Although NIRS cannot monitor deep brain structures such as the basal ganglia and cerebellum, its less onerous constraints are a characteristic advantage of this methodology. NIRS has been applied successfully in studies investigating the neural mechanisms for gait and postural control that are challenging to perform using other modalities. NIRS has also been utilized as a therapeutic tool in neurofeedback and brain–machine interface applications. Despite some shortcomings, NIRS could be a useful tool in the motor control study in a clinical setting, and might be effective as a therapeutic intervention.


Motor Imagery Cortical Activation Supplementary Motor Area Spinocerebellar Ataxia Deep Brain Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cope M, Delpy DT, Reynolds EO, Wray S, Wyatt J, van der Zee P. Methods of quantitating cerebral near infrared spectroscopy data. Adv Exp Med Biol. 1988;222:183–89.PubMedCrossRefGoogle Scholar
  2. 2.
    Maki A, Yamashita Y, Ito Y, Watanabe E, Mayanagi Y, Koizumi H. Spatial and temporal analysis of human motor activity using noninvasive NIR topography. Med Phys. 1995 Dec;22(12):1997–2005.PubMedCrossRefGoogle Scholar
  3. 3.
    Gratton G, Maier JS, Fabiani M, Mantulin WW, Gratton E. Feasibility of intracranial near-infrared optical scanning. Psychophysiology. 1994 Mar;31(2):211–5.PubMedCrossRefGoogle Scholar
  4. 4.
    Fox PT, Raichle ME. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proc Natl Acad Sci U S A. 1986 Feb;83(4):1140–4.PubMedCrossRefGoogle Scholar
  5. 5.
    Okamoto M, Dan H, Sakamoto K, et al. Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. Neuroimage. 2004 Jan;21(1):99–111.PubMedCrossRefGoogle Scholar
  6. 6.
    Takahashi T, Takikawa Y, Kawagoe R, Shibuya S, Iwano T, Kitazawa S. Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task. Neuroimage. 2011 Aug 1;57(3):991–1002.PubMedCrossRefGoogle Scholar
  7. 7.
    Kohno S, Miyai I, Seiyama A, et al. Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis. J Biomed Opt. 2007 Nov–Dec;12(6):062111.PubMedCrossRefGoogle Scholar
  8. 8.
    Yamada T, Umeyama S, Matsuda K. Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy. J Biomed Opt. 2009;14(6):064034.PubMedCrossRefGoogle Scholar
  9. 9.
    O’Loughlin JL, Robitaille Y, Boivin JF, Suissa S. Incidence of and risk factors for falls and injurious falls among the community-dwelling elderly. Am J Epidemiol. 1993 Feb 1;137(3):342–54.Google Scholar
  10. 10.
    Armstrong DM. The supraspinal control of mammalian locomotion. J Physiol. 1988 Nov;405:1–37.PubMedGoogle Scholar
  11. 11.
    Drew T, Prentice S, Schepens B. Cortical and brainstem control of locomotion. Prog Brain Res. 2004;143:251–61.PubMedCrossRefGoogle Scholar
  12. 12.
    Nielsen JB. How we walk: central control of muscle activity during human walking. Neuroscientist. 2003 Jun;9(3):195–204.PubMedCrossRefGoogle Scholar
  13. 13.
    Dietz V, Quintern J, Berger W. Cerebral evoked potentials associated with the compensatory reactions following stance and gait perturbation. Neurosci Lett. 1984 Sep 7;50(1–3):181–6.PubMedCrossRefGoogle Scholar
  14. 14.
    Quant S, Maki BE, McIlroy WE. The association between later cortical potentials and later phases of postural reactions evoked by perturbations to upright stance. Neurosci Lett. 2005 Jun 24;381(3):269–74.PubMedCrossRefGoogle Scholar
  15. 15.
    Slobounov S, Hallett M, Stanhope S, Shibasaki H. Role of cerebral cortex in human postural control: an EEG study. Clin Neurophysiol. 2005 Feb;116(2):315–23.PubMedCrossRefGoogle Scholar
  16. 16.
    Miyai I, Tanabe HC, Sase I, et al. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage. 2001 Nov;14(5):1186–92.PubMedCrossRefGoogle Scholar
  17. 17.
    Fukuyama H, Ouchi Y, Matsuzaki S, et al. Brain functional activity during gait in normal subjects: a SPECT study. Neurosci Lett. 1997 Jun 13;228(3):183–6.PubMedCrossRefGoogle Scholar
  18. 18.
    Suzuki M, Miyai I, Ono T, et al. Prefrontal and premotor cortices are involved in adapting walking and running speed on the treadmill: an optical imaging study. Neuroimage. 2004 Nov;23(3):1020–6.PubMedCrossRefGoogle Scholar
  19. 19.
    Mihara M, Miyai I, Hatakenaka M, Kubota K, Sakoda S. Role of the prefrontal cortex in human balance control. Neuroimage. 2008 Nov 1;43(2):329–36.PubMedCrossRefGoogle Scholar
  20. 20.
    Woollacott M, Shumway-Cook A. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture. 2002;16:1–14.PubMedCrossRefGoogle Scholar
  21. 21.
    Mihara M, Miyai I, Hattori N, et al. Cortical control of postural balance in patients with hemiplegic stroke. Neuroreport. 2012 Mar 28;23(5):314–9.PubMedCrossRefGoogle Scholar
  22. 22.
    Calautti C, Baron JC. Functional neuroimaging studies of motor recovery after stroke in adults: a review. Stroke. 2003 Jul;34(6):1553–66.PubMedCrossRefGoogle Scholar
  23. 23.
    Enzinger C, Dawes H, Johansen-Berg H, et al. Brain activity changes associated with treadmill training after stroke. Stroke. 2009 Jul;40(7):2460–7.PubMedCrossRefGoogle Scholar
  24. 24.
    Luft AR, Forrester L, Macko RF, et al. Brain activation of lower extremity movement in chronically impaired stroke survivors. Neuroimage. 2005 May 15;26(1):184–94.PubMedCrossRefGoogle Scholar
  25. 25.
    Ward NS, Brown MM, Thompson AJ, Frackowiak RS. Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain. 2003 Nov;126(Pt 11):2476–96.PubMedCrossRefGoogle Scholar
  26. 26.
    Doyon J, Benali H. Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol. 2005 Apr;15(2):161–7.PubMedCrossRefGoogle Scholar
  27. 27.
    Hatakenaka M, Miyai I, Mihara M, Sakoda S, Kubota K. Frontal regions involved in learning of motor skill—a functional NIRS study. Neuroimage. 2007 Jan 1;34(1):109–16.PubMedCrossRefGoogle Scholar
  28. 28.
    Grafton ST, Mazziotta JC, Presty S, Friston KJ, Frackowiak RS, Phelps ME. Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. J Neurosci. 1992 Jul;12(7):2542–8.PubMedGoogle Scholar
  29. 29.
    Mihara M, Miyai I, Haraguchi M, et al. Cortical network involved in the adaptation learning of reaching using 3-dimensional robotic rehabilitation system: a functional near-infrared spectroscopic study. Neuroimage. 2009;47(Suppl 1):S170.CrossRefGoogle Scholar
  30. 30.
    Furusho J, Koyanagi K, Imada Y, et al. A 3-D rehabilitation system for upper limbs. Developed in a 5-year NEDO project and its clinical testing. Paper presented at: IEEE 9th International Conference on Rehabilitation Robotics; 2005.Google Scholar
  31. 31.
    Buchel C, Holmes AP, Rees G, Friston KJ. Characterizing stimulus-response functions using nonlinear regressors in parametric fMRI experiments. Neuroimage. 1998 Aug;8(2):140–8.PubMedCrossRefGoogle Scholar
  32. 32.
    Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008 Nov;7(11):1032–43.PubMedCrossRefGoogle Scholar
  33. 33.
    Dobkin BH. Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol. 2007 Mar 15;579(Pt 3):637–42.PubMedCrossRefGoogle Scholar
  34. 34.
    Sitaram R, Zhang H, Guan C, et al. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. Neuroimage. 2007 Feb 15;34(4):1416–27.PubMedCrossRefGoogle Scholar
  35. 35.
    Mihara M, Miyai I, Hattori N, et al. Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation. PLoS One. 2012;7(3):e32234.PubMedCrossRefGoogle Scholar
  36. 36.
    Mihara M, Hattori N, Hatakenaka M, et al. Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study. Stroke. 2013;44(4):1091–1098.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Neurology, Graduate School of MedicineOsaka UniversitySuitaJapan
  2. 2.Neurorehabilitation Research InstituteMorinomiya HospitalJoto-kuJapan
  3. 3.Morinomiya HospitalJoto-kuJapan

Personalised recommendations