Method for functional MRI mapping of nonlinear response

NeuroImage

Volumn 19, Issue 1, 2003, Pages 190-199

  Kellman, Peter ^a   Van Gelderen, Peter ^b   De Zwart, Jacco A ^b   Duyn, Jozef H ^b  

^a NATIONAL HEART LUNG AND BLOOD INSTITUTE   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BLOOD OXYGENATION; BRAIN MAPPING; CONTROLLED STUDY; CORRELATION ANALYSIS; EXPERIMENTAL MODEL; HEMODYNAMICS; HUMAN; HUMAN EXPERIMENT; NONLINEAR SYSTEM; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PERIPHERAL VISION; PRIORITY JOURNAL; RETINA FOVEA; VISION; VISUAL STIMULATION;

EID: 0038777432     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1053-8119(03)00056-9     Document Type: Article

References (29)

1. Bandettini P.A., Jesmanowicz A., Wong E.C., Hyde J.S. Processing strategies for time-course data sets in functional MRI of the human brain. Magn. Reson. Med. 30:1993;161-173.
2. Baseler H.A., Sutter E.E. M and P components of the VEP and their visual field distribution. Vis. Res. 37:1997;675-690.
3. Benardete E.A., Kaplan E. The receptive field of the primate P retinal ganglion cell. II. Nonlinear dynamics. Vis. Neurosci. 14:1997;187-205.
4. Benardete E.A., Victor J.D. An extension of the m-sequence technique for the analysis of multi-input nonlinear systems. Marmarelis V.Z. Advanced Methods of Physiological System Modeling. Vol. III:1994;87-110 Plenum, New York.
5. Billings S.A. Identification of nonlinear systems - A survey. IEE Proc. 127:1980;272-285.
6. Birn R.M., Saad Z.S., Bandettini P.A. Spatial heterogeneity of the nonlinear dynamics in the FMRI BOLD response. NeuroImage. 14:2001;817-826.
7. Boynton G.M., Engel S.A., Glover G.H., Heeger D.J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16:1996;4207-4221.
8. Buracas, G.T., Boynton, G.M., 2002. Efficient design of event-related fMRI experiments using m-sequences. NeuroImage 16, 801-813.
9. Buxton R.B., Wong E.C., Frank L.R. Dynamics of blood flow and oxygenation changes during brain activation the balloon model . Magn. Reson. Med. 39:1998;855-864.
10. Derrington A.M., Lennie P. Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of Macaque. J. Physiol. 357:1984;219-240.
11. Emerson R.C., Korenberg M.J., Citron M.C. Identification of intensive nonlinearities in cascade models of visual cortex and its relation to cell classification. Marmarelis V.Z. Advanced Methods of Physiological System Modeling. Vol. II:1989;97-111 Plenum, New York.
12. Friston K.J., Jezzard P., Turner R. Analysis of functional MRI time series. Hum. Brain Mapp. 1:1994;153-171.
13. Friston K.J., Josephs O., Rees G., Turner R. Nonlinear event-related responses in fMRI. Magn. Reson. Med. 39:1998;41-52.
14. Friston K.J., Mechelli A., Turner R., Price C.J. Nonlinear responses in fMRI the balloon model, Volterra kernels, and other hemodynamics . NeuroImage. 12:2000;466-477.
15. Glover G.H. Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage. 9:1999;416-429.
16. Golomb S.W. Shift Register Sequences. 1968;Holden-Day, San Francisco.
17. Horton J.C., Hoyt W.F. The representation of the visual field in human striate cortex. A revision of the classic Holmes map. Arch. Ophthalmol. 109:1991;816-824.
18. Lee Y.W., Schetzen M. Measurement of the Wiener kernels of a non-linear system by cross-correlation. Int. J. Control. 2:1965;237-254.
19. Marmarelis P.Z., Marmarelis V.Z. Analysis of Physiological Systems - The White Noise Approach. 1978;Plenum, New York.
20. Marmarelis V.Z. Identification of nonlinear biological systems using Laguerre expansions of kernels. Ann. Biomed. Eng. 21:1993;573-589.
21. Miller K.L., Luh W.M., Liu T.T., Martinez A., Obata T., Wong E.C., Frank L.R., Buxton R.B. Nonlinear temporal dynamics of the cerebral blood flow response. Hum. Brain Mapp. 13:2001;1-12.
22. Ogawa S., Lee T.M., Stepnoski R., Chen W., Zhu X.H., Ugurbil K. An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proc. Natl. Acad. Sci. USA. 97:2000;11026-11031.
23. Reid R.C., Victor J.D., Shapley R.M. The use of m-sequences in the analysis of visual neurons linear receptive field properties . Vis. Neurosci. 14:1997;1015-1027.
24. Schetzen M. The Volterra and Wiener Theories of Nonlinear Systems. 1980;Wiley, New York.
25. Sutter E.E. A practical nonstochastic approach to nonlinear time-domain analysis. Marmarelis V.Z. Advanced Methods of Physiological System Modeling. Vol. 1:1987;303-315 Plenum, New York.
26. Sutter E.E. Imaging visual function with the multifocal m-sequence technique. Vis. Res. 41:2001;1241-1255.
27. Thévenaz P., Unser M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Proc. 7:1998;27-41.
28. Vazquez M.L., Noll D.C. Nonlinear aspects of the BOLD response in functional MRI. NeuroImage. 7:1998;108-118.
29. Wiener N. Nonlinear Problems in Random Theory. 1958;The Technology Press of MIT and Wiley, New York.