Single-trial reconstruction of finger-pinch forces from human motor-cortical activation measured by near-infrared spectroscopy (NIRS)

NeuroImage

Volumn 47, Issue 2, 2009, Pages 628-637

  Nambu, Isao ^a,b   Osu, Rieko ^a,c   Sato, Masa aki ^a   Ando, Soichi ^d   Kawato, Mitsuo ^a,b   Naito, Eiichi ^a,c  

^a ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^b NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^c NATIONAL INSTITUTE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY   (Japan)

^d OSAKA UNIVERSITY OF HEALTH AND SPORT SCIENCES   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

OXYHEMOGLOBIN;

ADULT; ARTICLE; CONTROLLED STUDY; FEMALE; FINGER; HAND STRENGTH; HUMAN; HUMAN EXPERIMENT; IMAGE RECONSTRUCTION; INDEX FINGER; MALE; MOTOR PERFORMANCE; MUSCLE ISOMETRIC CONTRACTION; NEAR INFRARED SPECTROSCOPY; NORMAL HUMAN; PINCH STRENGTH; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SENSORIMOTOR CORTEX; SENSORIMOTOR FUNCTION; SIGNAL PROCESSING; TEMPORAL CORTEX; THUMB;

ADULT; ALGORITHMS; BRAIN MAPPING; EVOKED POTENTIALS, MOTOR; FEMALE; FINGERS; HUMANS; MALE; MOTOR CORTEX; MUSCLE CONTRACTION; OXYHEMOGLOBINS; SENSITIVITY AND SPECIFICITY; SPECTROSCOPY, NEAR-INFRARED; STRESS, MECHANICAL; TASK PERFORMANCE AND ANALYSIS; YOUNG ADULT;

EID: 67349172155     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.04.050     Document Type: Article

References (51)

1. Aguirre G.K., Zarahn E., and D'Esposito M. The variability of human BOLD hemodynamic responses. NeuroImage 8 4 (1998) 360-369
2. Attias, H., 1999. Inferring parameters and structure of latent variable models by variational Bayes. Proc. 15th Conference on Uncertainty in Artificial Intelligence, Morgan-Kauffman, pp. 21-30.
3. Birbaumer N., and Cohen L.G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579 (2007) 621-636
4. Boas D.A., Dale A.M., and Franceschini M.A. Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy. NeuroImage 23 Suppl 1 (2004) S275-S288
5. Carmena J.M., Lebedev M.A., Crist R.E., O'Doherty J.E., Santucci D.M., Dimitrov D.F., Patil P.G., Henriquez C.S., and Nicolelis M.A. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1 (2003) e42
6. Cheney P.D., and Fetz E.E. Functional classes of primate corticomotoneuronal cells and their relation to active force. J. Neurophysiol. 44 (1980) 773-791
7. Coyle S., Ward T., Markham C., and McDarby G. On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. Physiol. Meas. 25 (2004) 815-822
8. Coyle S.M., Ward T.E., and Markham C.M. Brain-computer interface using a simplified functional near-infrared spectroscopy system. J. Neural Eng. 4 (2007) 219-226
9. Cramer S.C., Weisskoff R.M., Schaechter J.D., Nelles G., Foley M., Finklestein S.P., and Rosen B.R. Motor cortex activation is related to force of squeezing. Hum. Brain Mapp. 16 (2002) 197-205
10. Dai T.H., Liu J.Z., Sahgal V., Brown R.W., and Yue G.H. Relationship between muscle output and functional MRI-measured brain activation. Exp. Brain Res. 140 (2001) 290-300
11. Dettmers C., Fink G.R., Lemon R.N., Stephan K.M., Passingham R.E., Silbersweig D., Holmes A., Ridding M.C., Brooks D.J., and Frackowiak R.S. Relation between cerebral activity and force in the motor areas of the human brain. J. Neurophysiol. 74 (1995) 802-815
12. Dettmers C., Connelly A., Steohan K.M., Turner R., Friston K.J., Frackowiak R.S., and Gadian D.G. Quantitative comparison of functional magnetic resonance imaging with positron emission tomography using a force-related paradigm. NeuroImage 4 3 (1996) 201-209
13. Ehrsson H.H., Fagergren A., Jonsson T., Westking G., Johansson R.S., and Forssberg H. Cortical activity in precision- versus power-grip tasks: an fMRI study. J. Neurophysiol. 83 1 (2000) 528-536
14. Evarts E.V. Relation of pyramidal tract activity to force exerted during voluntary movement. J. Neurophysiol. 31 (1968) 14-27
15. Ganesh G., Burdet E., Haruno M., and Kawato M. Sparse linear regression for reconstructing muscle activity from human cortical fMRI. NeuroImage 42 4 (2008) 1463-1472
16. Handwerker D.A., Ollinger J.M., and D ' Esposito M. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage 21 4 (2004) 1639-1651
17. Hatakenaka M., Miyai I., Mihara M., Sakoda S., and Kubota K. Frontal regions involved in learning of motor skill-a functional NIRS study. NeuroImage 34 1 (2007) 109-116
18. Hoshi Y. Functional near-infrared optical imaging: utility and limitations in human brain mapping. Psychophysiology 40 (2003) 511-520
19. Hoshi Y., Kobayashi N., and Tamura M. Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model. J. Appl. Physiol. 90 (2001) 1657-1662
20. Huppert T.J., Hoge R.D., Diamond S.G., Franceschini M.A., and Boas D.A. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. NeuroImage 29 2 (2006) 368-382
21. Ikeda A., Lüders H.O., Burgess R.C., and Shibasaki H. Movement-related potentials recorded from supplementary motor area and primary motor area. Role of supplementary motor area in voluntary movements. Brain 15 4 (1992) 1017-1043
22. Koike Y., Hirose H., Sakurai Y., and Iijima T. Prediction of arm trajectory from a small number of neuron activities in the primary motor cortex. Neurosci. Res. 55 (2006) 146-153
23. Koizumi H., Yamashita Y., Maki A., Yamamoto T., and Ito Y. Higher-order brain function analysis by trans-cranial dynamic near-infrared spectroscopy imaging. J. Biomed. Opt. 4 4 (1999) 403-413
24. Koizumi H., Yamamoto T., Maki A., Yamashita Y., Sato H., Kawaguchi H., and Ichikawa N. Optical topography: practical problems and new applications. Appl. Opt. 42 (2003) 3054-3062
25. Kohno S., Miyai I., Seiyama A., Oda I., Ishikawa A., Tsuneishi S., Amita T., and Shimizu K. Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis. J. Biomed. Opt. 12 6 (2007) 062111
26. Leung TS., Elwell CE., Henty J., and Delpy DT. Simultaneous measurement of cerebral tissue oxygenation over the adult frontal and motor cortex during rest and functional activation. Adv. Exp. Med. Biol. 510 (2003) 385-389
27. Maki A., Yamashita Y., Ito Y., Watanabe E., Mayanagi Y., and Koizumi H. Spatial and temporal analysis of human motor activity using noninvasive NIR topography. Med. Phys. 22 (1995) 1997-2005
28. Miezin F.M., Maccotta L., Ollinger J.M., Petersen S.E., and Buckner R.L. Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. NeuroImage 11 6 (2000) 735-759
29. Miyai I., Tanabe H.C., Sase I., Eda H., Oda I., Konishi I., Tsunazawa Y., Suzuki T., Yanagida T., and Kubota K. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. NeuroImage 14 5 (2001) 1186-1192
30. Morrow M.M., and Miller L.E. Prediction of muscle activity by populations of sequentially recorded primary motor cortex neurons. J. Neurophysiol. 89 (2003) 2279-2288
31. Neal R.M. Bayesian learning for neural networks. Lect. Notes Stat. 118 (1996) Springer
32. Obrig H., Hirth C., Junge-Hülsing J.G., Döge C., Wolf T., Dirnagl U., and Villringer A. Cerebral oxygenation changes in response to motor stimulation. J. Appl. Physiol. 81 3 (1996) 1174-1183
33. Obrig H., Neufang M., Wenzel R., Kohl M., Steinbrink J., Einhäupl K., and Villringer A. Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. NeuroImage 12 6 (2000) 623-639
34. Obrig H., and Villringer A. Beyond the visible-imaging the human brain with light. J. Cereb. Blood Flow Metab. 23 (2003) 1-18
35. Sato M.A. Online model selection based on the variational Bayes. Neural Comp. 13 (2001) 1649-1681
36. Sato H., Kiguchi M., Kawaguchi F., and Maki A. Practicality of wavelength selection to improve signal-to-noise ratio in near-infrared spectroscopy. NeuroImage 21 4 (2004) 1554-1562
37. Sato M.A., Yoshioka T., Kajihara S., Toyama K., Goda N., Doya K., and Kawato M. Hierarchical Bayesian estimation for MEG inverse problem. NeuroImage 23 3 (2004) 806-826
38. Sitaram R., Zhang H., Guan C., Thulasidas M., Hoshi Y., Ishikawa A., Shimizu K., and Birbaumer N. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. NeuroImage 34 4 (2007) 1416-1427
39. Tachtsidis I., Leung T.S., Tisdall M.M., Devendra P., Smith M., Delpy D.T., and Elwell C.E. Investigation of frontal cortex, motor cortex and systemic haemodynamic changes during anagram solving. Adv. Exp. Med. Biol. 614 (2008) 21-28
40. Tachtsidis I., Leung T.S., Devoto L., Delpy D.T., and Elwell C.E. Measurement of frontal lobe functional activation and related systemic effects: a near-infrared spectroscopy investigation. Adv. Exp. Med. Biol. 614 (2008) 397-403
41. Thickbroom G.W., Phillips B.A., Morris I., Byrnes M.L., Sacco P., and Mastaglia F.L. Differences in functional magnetic resonance imaging of sensorimotor cortex during static and dynamic finger flexion. Exp. Brain Res. 126 (1998) 431-438
42. Ting J.A., D'Souza A., Yamamoto K., Yoshioka T., Hoffman D.S., Kakei S., Lauren S., Kalaska J.F., Kawato M., Strick P.L., and Schaal S. Predicting EMG data from M1 neurons with Variational Bayesian least squares. Proceedings of Advances in Neural Information Processing Systems 18 (NIPS2005) (2005), MIT press, Cambridge, MA
43. Ting J.A., D'Souza A., Yamamoto K., Yoshioka T., Hoffman D.S., Kakei S., Lauren S., Kalaska J.F., Kawato M., Strick P.L., and Schaal S. Variational Bayesian least squares: an application to brain-machine interface data. Neural Networks 21 8 (2008) 1112-1131
44. Toda, A., Imamizu, H., Sato, M., Wada, Y., Kawato, M., 2007. Reconstruction of temporal movement from single-trial non-invasive brain activity: a hierarchical Bayesian method. The 14th International Conference on Neural Information Processing (ICONIP2007).
45. Townsend B.R., Paninski L., and Lemon R.N. Linear encoding of muscle activity in primary motor cortex and cerebellum. J. Neurophysiol. 96 5 (2006) 2578-2592
46. Villringer A., and Chance B. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 20 10 (1997) 435-442
47. Ward T.E., Soraghan C.J., Matthews F., and Markham C. A concept for extending the applicability of constraint-induced movement therapy through motor cortex activity feedback-using a neural prosthesis. Comput. Intell. Neurosci. (2007) Article ID 51363, 9 p
48. Watanabe E., Yamashita Y., Maki A., Ito Y., and Koizumi H. Non-invasive functional mapping with multi-channel near infra-red spectroscopic topography in humans. Neurosci. Lett. 205 1 (1996) 41-44
49. Wolf M., Wolf U., Choi J.H., Gupta R., Safonova L.P., Paunescu L.A., Michalos A., and Gratton E. Functional frequency-domain near-infrared spectroscopy detects fast neuronal signal in the motor cortex. NeuroImage 17 4 (2002) 1868-1875
50. Yamashita O., Sato M.A., Yoshioka T., Tong F., and Kamitani Y. Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. NeuroImage 42 4 (2008) 1414-1429
51. Zhang Y., Brooks DH., Franceschini MA., and Boas DA. Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging. J. Biomed. Opt. 10 1 (2005) 11014