State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices

Biomedical Optics Express

Volumn 5, Issue 6, 2014, Pages 1778-1798

  Hong, Keum Shik ^a   Nguyen, Hoang Dung ^a  

^a Pusan National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; STATE SPACE METHODS;

HEALTHY SUBJECTS; HEMODYNAMIC RESPONSE; OXY-HEMOGLOBIN; SOMATOSENSORY CORTEX; STATE - SPACE MODELS; STATE SPACE EQUATION; SUB-SPACE METHODS; VISUAL CORTEXES;

EQUATIONS OF STATE;

ADULT; ARTICLE; DATA PROCESSING; FEMALE; HEMODYNAMICS; HUMAN; IMAGE ANALYSIS; MALE; MATHEMATICAL MODEL; MOTOR CORTEX; NERVE CONDUCTION; NEUROLOGIC EXAMINATION; NORMAL HUMAN; OPTICAL DENSITY; RIGHT MIDDLE FINGER TAPPING; SIGNAL NOISE RATIO; SOMATOSENSORY CORTEX; STIMULUS RESPONSE; VISUAL CORTEX;

EID: 84917733894     PISSN: None     EISSN: 21567085     Source Type: Journal    
DOI: 10.1364/BOE.5.001778     Document Type: Article

References (38)

1. K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, "Event-related fMRI: characterizing differential responses," Neuroimage 7(1), 30-40 (1998).
2. M. A. Lindquist, J. Meng Loh, L. Y. Atlas, and T. D. Wager, "Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling," Neuroimage 45(1 Suppl), S187-S198 (2009).
3. H. F. Chen, D. Z. Yao, and Z. X. Liu, "A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response," Magn. Reson. Imaging 23(1), 83-88 (2005).
4. K. Ciftçi, B. Sankur, Y. P. Kahya, and A. Akin, "Constraining the general linear model for sensible hemodynamic response function waveforms," Med. Biol. Eng. Comput. 46(8), 779-787 (2008).
5. X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, "Kalman estimator-and general linear model-based on-line brain activation mapping by near-infrared spectroscopy," Biomed. Eng. Online 9(1), 82 (2010).
6. R. B. Buxton, E. C. Wong, and L. R. Frank, "Dynamics of blood flow and oxygenation changes during brain activation: the balloon model," Magn. Reson. Med. 39(6), 855-864 (1998).
7. K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, "Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics," Neuroimage 12(4), 466-477 (2000).
8. K. J. Friston, L. Harrison, and W. Penny, "Dynamic causal modelling," Neuroimage 19(4), 1273-1302 (2003).
9. K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, "Comparing hemodynamic models with DCM," Neuroimage 38(3), 387-401 (2007).
10. J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, "Activation detection in diffuse optical imaging by means of the general linear model," Med. Image Anal. 11(6), 616-629 (2007).
11. M. A. Kamran and K. S. Hong, "Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study," J. Neural Eng. 10(5), 056002 (2013).
12. N. Naseer, M. J. Hong, and K. S. Hong, "Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface," Exp. Brain Res. 232(2), 555-564 (2014).
13. X. S. Hu, K. S. Hong, and S. S. Ge, "Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series," Neurosci. Lett. 504(2), 115-120 (2011).
14. T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. A. Sato, "Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact," Biomed. Opt. Express 4(11), 2411-2432 (2013).
15. R. Re, D. Contini, M. Turola, L. Spinelli, L. Zucchelli, M. Caffini, R. Cubeddu, and A. Torricelli, "Multichannel medical device for time domain functional near infrared spectroscopy based on wavelength space multiplexing," Biomed. Opt. Express 4(10), 2231-2246 (2013).
16. M. Cope and D. T. Delpy, "System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination," Med. Biol. Eng. Comput. 26(3), 289-294 (1988).
17. R. B. Saager, N. L. Telleri, and A. J. Berger, "Two-detector corrected near infrared spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS," Neuroimage 55(4), 1679-1685 (2011).
18. J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, "NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy," Neuroimage 44(2), 428-447 (2009).
19. M. S. Hassanpour, B. R. White, A. T. Eggebrecht, S. L. Ferradal, A. Z. Snyder, and J. P. Culver, "Statistical analysis of high density diffuse optical tomography," Neuroimage 85(Pt 1), 104-116 (2014).
20. M. Aqil, K. S. Hong, M. Y. Jeong, and S. S. Ge, "Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity," Neuroimage 63(1), 553-568 (2012).
21. X. S. Hu, K. S. Hong, and S. S. Ge, "fNIRS-based online deception decoding," J. Neural Eng. 9(2), 026012 (2012).
22. I. Schelkanova and V. Toronov, "Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head," Biomed. Opt. Express 3(1), 64-74 (2012).
23. Z. Yuan, "Combining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements," Biomed. Opt. Express 4(11), 2629-2643 (2013).
24. N. Naseer and K. S. Hong, "Classification of functional near-infrared spectroscopy signals corresponding to the right-and left-wrist motor imagery for development of a brain-computer interface," Neurosci. Lett. 553, 84-89 (2013).
25. H. Santosa, M. J. Hong, S. P. Kim, and K. S. Hong, "Noise reduction in functional near-infrared spectroscopy signals by independent component analysis," Rev. Sci. Instrum. 84(7), 073106 (2013).
26. A. Villringer and B. Chance, "Non-invasive optical spectroscopy and imaging of human brain function," Trends Neurosci. 20(10), 435-442 (1997).
27. G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, "Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy," Neuroimage 20(1), 479-488 (2003).
28. Y. Hoshi, N. Kobayashi, and M. Tamura, "Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model," J. Appl. Physiol. 90(5), 1657-1662 (2001).
29. S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, "Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data," Neuroimage 85(Pt 1), 181-191 (2014).
30. J. W. Barker, A. Aarabi, and T. J. Huppert, "Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS," Biomed. Opt. Express 4(8), 1366-1379 (2013).
31. X. S. Hu, K. S. Hong, and S. S. Ge, "Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity," J. Biomed. Opt. 18(1), 017003 (2013).
32. M. A. Franceschini, S. Fantini, J. H. Thompson, J. P. Culver, and D. A. Boas, "Hemodynamic evoked response of the sensorimotor cortex measured noninvasively with near-infrared optical imaging," Psychophysiology 40(4), 548-560 (2003).
33. T. Katayama, Subspace methods for system identification, E. D. Sontag, M. Thoma, A. Isidori, J. H. vanSchuppen ed. (Springer-Verlag London Limited, 2005).
34. F. M. Miezin, L. Maccotta, J. M. Ollinger, S. E. Petersen, and R. L. Buckner, "Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing," Neuroimage 11(6), 735-759 (2000).
35. M. R. Bhutta, K. S. Hong, B. M. Kim, M. J. Hong, Y. H. Kim, and S. H. Lee, "Note: three wavelengths nearinfrared spectroscopy system for compensating the light absorbance by water," Rev. Sci. Instrum. 85(2), 026111 (2014).
36. M. J. Khan, M. J. Hong, and K. S. Hong, "Decoding of four movement directions using hybrid NIRS-EEG braincomputer interface," Front. Hum. Neurosci. 8, 244 (2014).
37. P. Baraldi, A. A. Manginelli, M. Maieron, D. Liberati, and C. A. Porro, "An ARX model-based approach to trial by trial identification of fMRI-BOLD responses," Neuroimage 37(1), 189-201 (2007).
38. E. Yacoub, A. Shmuel, J. Pfeuffer, P. F. Van De Moortele, G. Adriany, K. Ugurbil, and X. P. Hu, "Investigation of the initial dip in fMRI at 7 Tesla," NMR Biomed. 14(7-8), 408-412 (2001).