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Journal of NeuroEngineering and Rehabilitation
Published in: Journal of NeuroEngineering and Rehabilitation 1/2010

Open Access 01-12-2010 | Research

Testing the potential of a virtual reality neurorehabilitation system during performance of observation, imagery and imitation of motor actions recorded by wireless functional near-infrared spectroscopy (fNIRS)

Authors: Lisa Holper, Thomas Muehlemann, Felix Scholkmann, Kynan Eng, Daniel Kiper, Martin Wolf

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2010

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Abstract

Background

Several neurorehabilitation strategies have been introduced over the last decade based on the so-called simulation hypothesis. This hypothesis states that a neural network located in primary and secondary motor areas is activated not only during overt motor execution, but also during observation or imagery of the same motor action. Based on this hypothesis, we investigated the combination of a virtual reality (VR) based neurorehabilitation system together with a wireless functional near infrared spectroscopy (fNIRS) instrument. This combination is particularly appealing from a rehabilitation perspective as it may allow minimally constrained monitoring during neurorehabilitative training.

Methods

fNIRS was applied over F3 of healthy subjects during task performance in a virtual reality (VR) environment: 1) 'unilateral' group (N = 15), contralateral recording during observation, motor imagery, observation & motor imagery, and imitation of a grasping task performed by a virtual limb (first-person perspective view) using the right hand; 2) 'bilateral' group (N = 8), bilateral recording during observation and imitation of the same task using the right and left hand alternately.

Results

In the unilateral group, significant within-condition oxy-hemoglobin concentration Δ[O2Hb] changes (mean ± SD μmol/l) were found for motor imagery (0.0868 ± 0.5201 μmol/l) and imitation (0.1715 ± 0.4567 μmol/l). In addition, the bilateral group showed a significant within-condition Δ[O2Hb] change for observation (0.0924 ± 0.3369 μmol/l) as well as between-conditions with lower Δ[O2Hb] amplitudes during observation compared to imitation, especially in the ipsilateral hemisphere (p < 0.001). Further, in the bilateral group, imitation using the non-dominant (left) hand resulted in larger Δ[O2Hb] changes in both the ipsi- and contralateral hemispheres as compared to using the dominant (right) hand.

Conclusions

This study shows that our combined VR-fNIRS based neurorehabilitation system can activate the action-observation system as described by the simulation hypothesis during performance of observation, motor imagery and imitation of hand actions elicited by a VR environment. Further, in accordance with previous studies, the findings of this study revealed that both inter-subject variability and handedness need to be taken into account when recording in untrained subjects. These findings are of relevance for demonstrating the potential of the VR-fNIRS instrument in neurofeedback applications.
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Literature
1.
Ertelt D, et al.: Action observation has a positive impact on rehabilitation of motor deficits after stroke. NeuroImage 2007,36(Suppl 2):T164-T173. 10.1016/j.neuroimage.2007.03.043CrossRefPubMed
2.
De Vries S, Mulder T: Motor imagery and stroke rehabilitation: a critical discussion. J Rehabil Med 2007,37(1):5-13. 10.2340/16501977-0020CrossRef
3.
Buccino G, Solodkin A, Small S: Functions of the mirror neuron system: implications for neurorehabilitation. Cogn Behav Neurol 2006,19(1):55-63. 10.1097/00146965-200603000-00007CrossRefPubMed
4.
Jeannerod M: The representing brain: Neural correlates of motor intention and imagery. Behav Brain Res 1994, 17: 187-245. 10.1017/S0140525X00034026
5.
Rizzolatti G, et al.: Premotor cortex and the recognition of motor actions. Brain Res Cogn Brain Res 1996,3(2):131-41. 10.1016/0926-6410(95)00038-0CrossRefPubMed
6.
Lotze M, et al.: Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study. J Cogn Neurosci 1999,11(5):491-501. 10.1162/089892999563553CrossRefPubMed
7.
Rizzolatti G, et al.: Premotor cortex and the recognition of motor actions. Brain Res Cogn Brain Res 1999,3(2):131-142. 10.1016/0926-6410(95)00038-0CrossRef
8.
Fadiga L, et al.: Motor facilitation during action observation: a magnetic stimulation study. J Neurophysiol 1995,73(6):2608-2611.PubMed
9.
Eng K, et al.: Interactive visuo-motor therapy system for stroke rehabilitation. Med Biol Eng Comput 2007, 45: 901-907. 10.1007/s11517-007-0239-1CrossRefPubMed
10.
Hoshi Y, Tamura M: Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in men. Neuroscience Letters 1993, 150: 5-8. 10.1016/0304-3940(93)90094-2CrossRefPubMed
11.
Wolf M, Ferrari M, Quaresima V: Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications. J Biomed Opt 2007,12(6):062104. 10.1117/1.2804899CrossRefPubMed
12.
Muehlemann T, Haensse D, Wolf M: Wireless miniaturized in-vivo near infrared imaging. Optics Express 2008,16(14):10323-30. 10.1364/OE.16.010323CrossRefPubMed
13.
14.
Filimon F, et al.: Human cortical representations for reaching: Mirror neurons for execution, observation, and imagery. NeuroImage 2007,37(4):1315-1328. 10.1016/j.neuroimage.2007.06.008PubMedCentralCrossRefPubMed
15.
Gazzola V, Keysers C: The Observation and Execution of Actions Share Motor and Somatosensory Voxels in all Tested Subjects: Single-Subject Analyses of Unsmoothed fMRI Data. Cereb Cortex 2009,19(6):1239-1255. 10.1093/cercor/bhn181PubMedCentralCrossRefPubMed
16.
Shimada S, Abe R: Modulation of the motor area activity during observation of a competitive game. NeuroReport 2009,20(11):979-983. 10.1097/WNR.0b013e32832d2d36CrossRefPubMed
17.
Shimada S, Hiraki K: Infant's brain responses to live and televised action. Neuroimage 2006,32(2):930-939. 10.1016/j.neuroimage.2006.03.044CrossRefPubMed
18.
Shibata H, Suzuki M, Gyoba J: Cortical activity during the recognition of cooperative actions. Cog Neuroscience Neuropsychology 2007,18(7):697-701.
19.
Shiraishi T, et al.: Observation and imitation of nursing actions: A NIRS study with experts and novices. Stud Health Technol Inform 2006, 122: 820-1.PubMed
20.
Fuchino Y, et al.: High cognitive function of an ALS patient in the totally locked-in state. Neuroscience Letters 2008,435(2):85-89. 10.1016/j.neulet.2008.01.046CrossRefPubMed
21.
Miyai I, et al.: Cortical Mapping of Gait in Humans: A Near-Infrared Spectroscopic Topography Study. NeuroImage 2001,14(5):1186-1192. 10.1006/nimg.2001.0905CrossRefPubMed
22.
Cooper C, et al.: NIRS-detected changes in the motor cortex during mental rehearsal of physical activity (imaginary exercise). Adv Exp Med Biol 2006, 578: 185-90. full_textCrossRefPubMed
23.
Wriessnegger SC, Kurzmann J, Neuper C: Spatio-temporal differences in brain oxygenation between movement execution and imagery: A multichannel near-infrared spectroscopy study. International Journal of Psychophysiology 2008,67(1):54-63. 10.1016/j.ijpsycho.2007.10.004CrossRefPubMed
24.
Sitaram R, et al.: Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. NeuroImage 2007,34(4):1416-1427. 10.1016/j.neuroimage.2006.11.005CrossRefPubMed
25.
Coyle S, Ward T, Markham C: Brain-computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng 2007,4(3):219-26. 10.1088/1741-2560/4/3/007CrossRefPubMed
26.
Coyle S, et al.: On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. Physiol Meas 2004,25(4):815-22. 10.1088/0967-3334/25/4/003CrossRefPubMed
27.
Kanoh S, et al.: A NIRS-based brain-computer interface system during motor imagery: System development and online feedback training. Engineering in Medicine and Biology Society EMB, Annual International Conference of the IEEE 2009.
28.
Oldfield R: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971,9(1):97-113. 10.1016/0028-3932(71)90067-4CrossRefPubMed
29.
30.
Colebatch JG, et al.: Regional cerebral blood flow during voluntary arm and hand movements in human subjects. J Neurophysiol 1991,65(6):1392-1401.PubMed
31.
Pulvermuller F, et al.: Motor programming in both hemispheres: an EEG study of the human brain. Neuroscience Letters 1995, 190: 5-8. 10.1016/0304-3940(95)11486-GCrossRefPubMed
32.
Rao SM, et al.: Functional magnetic resonance imaging of complex human movements. Neurology 1993,43(11):2311.CrossRefPubMed
33.
Solodkin A, et al.: Lateralization of motor circuits and handedness during finger movements. European Journal of Neurology 2001, 8: 425-434. 10.1046/j.1468-1331.2001.00242.xCrossRefPubMed
34.
Muehlemann T, et al.: In vivo functional near-infrared spectroscopy measures mood-modulated cerebral responses to a positive emotional stimulus in sheep. NeuroImage 2010,54(2):1625-1633. 10.1016/j.neuroimage.2010.08.079CrossRefPubMed
35.
Jaspers H: The ten-twenty electrode system of the International Federation. In: Electroencephalography and clinical neurophysiology. Electroencephalogr Clin Neurophysiol 1958, 10: 371-375.
36.
Buccino G, Riggio L: The role of the mirror neuron system in motor learning. Kinesiology 2006,38(1):5-15.
37.
Delpy D, et al.: Estimation of optical path length through tissue from direct time of flight measurements. Phys Med Biol 1988, 33: 1433-42. 10.1088/0031-9155/33/12/008CrossRefPubMed
38.
Wray S, et al.: Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation. Biochimica et Biophysica Acta (BBA) - Bioenergetics 1988,933(1):184-192. 10.1016/0005-2728(88)90069-2CrossRef
39.
Zhao H, et al.: Maps of optical differential pathlength factor of human adult forehead, somatosensory motor and occipital regions at multi-wavelengths in NIR. Phys Med Biol 2002, 47: 2075-2093. 10.1088/0031-9155/47/12/306CrossRefPubMed
40.
Strangman G, et al.: A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage 2002, 17: 719-731. 10.1006/nimg.2002.1227CrossRefPubMed
41.
42.
Hoshi Y, Chen S: New Dimension of Cognitive Neuroscience Research with Near-Infrared Spectroscopy: Free-Motion Neuroimaging Studies. In Progress in Brain Mapping Research. 1st edition. Edited by: Science N. New York; 2006:205-229.
43.
Beisteiner R, et al.: Mental representations of movements. Brain potentials associated with imagination of hand movements. Electroencephalogr Clin Neurophysiol 1995,96(2):183-93. 10.1016/0168-5597(94)00226-5CrossRefPubMed
44.
Grèzes J, Decety J: Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta-analysis. Hum Brain Mapp 2001,12(1):1-19. 10.1002/1097-0193(200101)12:1<1::AID-HBM10>3.0.CO;2-VCrossRefPubMed
45.
Holper L, Wolf M: Motor imagery in response to fake feedback measured by functional near-infrared spectroscopy. Neuroimage 2010,50(1):190-7. 10.1016/j.neuroimage.2009.12.055CrossRefPubMed
46.
Okada E, Delpy D: Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. Appl Opt 2003,42(16):2906-14. 10.1364/AO.42.002906CrossRefPubMed
47.
Okada E, Delpy D: Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal. Appl Opt 2003,42(16):2915-22. 10.1364/AO.42.002915CrossRefPubMed
48.
Sirigu A, et al.: The mental representation of hand movements after parietal cortex damage. Science 1996,273(5281):1564-8. 10.1126/science.273.5281.1564CrossRefPubMed
49.
Malouin F, et al.: Bilateral slowing of mentally simulated actions after stroke. NeuroReport 2004,15(8):1349-53.CrossRefPubMed
50.
Sabaté M, González B, Rodríguez M: Brain lateralization of motor imagery: motor planning asymmetry as a cause of movement lateralization. Neuropsychologia 2004,42(8):1041-1049. 10.1016/j.neuropsychologia.2003.12.015CrossRefPubMed
51.
Ang K, et al.: A clinical evaluation on the spatial patterns of non-invasive motor imagery-based brain-computer interface in stroke. Conf Proc IEEE Eng Med Biol Soc 2008.
52.
Liang N, et al.: Further evidence for excitability changes in human primary motor cortex during ipsilateral voluntary contractions. Neuroscience Letters 2008,433(2):135-140. 10.1016/j.neulet.2007.12.058CrossRefPubMed
53.
Babiloni F, et al.: Brain computer interface: estimation of cortical activity from non invasive high resolution EEG recordings. Conf Proc IEEE Eng Med Biol Soc 2004.
54.
Holper L, Biallas M, Wolf M: Task complexity relates to activation of cortical motor areas during uni- and bimanual performance: A functional NIRS study. NeuroImage 2009,46(4):1105-1113. 10.1016/j.neuroimage.2009.03.027CrossRefPubMed
55.
Hess CW, Mills KR, Murray NMF: Magnetic stimulation of the human brain: Facilitation of motor responses by voluntary contraction of ipsilateral and contralateral muscles with additional observations on an amputee. Neuroscience Letters 1986,71(2):235-240. 10.1016/0304-3940(86)90565-3CrossRefPubMed
56.
Muellbacher W, et al.: Changes in motor cortex excitability during ipsilateral hand muscle activation in humans. Clinical Neurophysiology 2000,111(2):344-349. 10.1016/S1388-2457(99)00243-6CrossRefPubMed
57.
Tinazzi M, Zanette G: Modulation of ipsilateral motor cortex in man during unimanual finger movements of different complexities. Neuroscience Letters 1998,244(3):121-124. 10.1016/S0304-3940(98)00150-5CrossRefPubMed
58.
Shibuya K, Kuboyama N: Human motor cortex oxygenation during exhaustive pinching task. Brain Research 2007, 1156: 120-124. 10.1016/j.brainres.2007.05.009CrossRefPubMed
59.
Cramer SC, et al.: Activation of Distinct Motor Cortex Regions During Ipsilateral and Contralateral Finger Movements. J Neurophysiol 1999,81(1):383-387.PubMed
60.
Verstynen T, et al.: Ipsilateral Motor Cortex Activity During Unimanual Hand Movements Relates to Task Complexity. J Neurophysiol 2005,93(3):1209-1222. 10.1152/jn.00720.2004CrossRefPubMed
61.
Mattay V, et al.: Hemispheric control of motor function: a whole brain echo planar fMRI study. Psychiatry Res 1998,83(1):7-22. 10.1016/S0925-4927(98)00023-7CrossRefPubMed
62.
Singh L, et al.: Comparison of ipsilateral activation between right and left handers: a functional MR imaging study. [Miscellaneous Article]. Neuroreport 1998,9(8):1861-6. 10.1097/00001756-199806010-00036CrossRefPubMed
63.
Porro C, et al.: Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study. J Neurosci 1996,16(23):7688-7698.PubMed
Metadata
Title
Testing the potential of a virtual reality neurorehabilitation system during performance of observation, imagery and imitation of motor actions recorded by wireless functional near-infrared spectroscopy (fNIRS)
Authors
Lisa Holper
Thomas Muehlemann
Felix Scholkmann
Kynan Eng
Daniel Kiper
Martin Wolf
Publication date
01-12-2010
Publisher
BioMed Central
Published in
Journal of NeuroEngineering and Rehabilitation / Issue 1/2010
Electronic ISSN: 1743-0003
DOI
https://doi.org/10.1186/1743-0003-7-57

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