Modeling the haemodynamic response in fMRI using smooth FIR filters

IEEE Transactions on Medical Imaging

Volumn 19, Issue 12, 2000, Pages 1188-1201

  Goutte, Cyril ^a   Nielsen, Finn Årup ^a   Hansen, Lars Kai ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Evidence; Fir filters; Fmri; Haemodynamic response; Markov chain monte carlo; Neuroimaging; Smoothness prior; Tikhonov regularization

Indexed keywords

FIR FILTERS; HEMODYNAMICS; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; MONTE CARLO METHODS;

TIKHONOV REGULARIZATION;

MEDICAL IMAGING;

ARTICLE; HEMODYNAMICS; HUMAN; MONTE CARLO METHOD; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PHOTOSTIMULATION; PHYSIOLOGY; PROBABILITY;

HEMODYNAMIC PROCESSES; HUMANS; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MONTE CARLO METHOD; NORMAL DISTRIBUTION; PHOTIC STIMULATION;

EID: 0034351115     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/42.897811     Document Type: Article

References (37)

1. P. A. Bandettini and R. W. CoxFunctional contrast in event-related fMRI: Interstimulus dependency and blocked design comparison, in Proceedings of the Fourth International Conference on Functional Mapping of the Human Brain, ser. Neurolmage, T. Paus, A. Gjedde, and A. Evans, Eds. New York: Academic, May 1998, pt. 2 of 3, p. 522.
2. C. G. Thomas and R. S. MenonAmplitude response and stimulus presentation frequency response of human primary visual cortex using BOLD EPI at 4 T, Magn. Reson. Medicine, vol. 40, no. 2, pp. 203-209, 1998.
3. R. B. Buxton, E. C. Wong, and L. R. FrankDynamics of blood flow and oxygénation changes during brain activation: The ballon model, Magn. Reson. Med, vol. 39, no. 6, pp. 855-864, 1998.
4. G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. HeegerLinear systems analysis of functional magnetic resonance imaging in human VI, J. Neurosci, vol. 16, no. 13, pp. 4207-4221, 1996.
5. J. R. Binder, S. M. Rao, T. A. Hammeke, J. A. Frost, P. A. Bandettini, andJ. S. HydeEffects of stimulus on signal response during functional magnetic-resonance-imaging of auditory-cortex, Cogn. Brain Res.,vol. 2, no. 1, pp. 31-38, 1994.
6. A. M. Dale and R. L. BucknerSelective averaging of rapidly presented individual trials using fMRI, Human Brain Mapping, vol. 5, no. 5, pp. 329-340, 1997.
7. M. D. Robson, J. L. Dorosz, and J. C. GoreMeasurements of the temporal fMRI response of the human auditory cortex to trains of tones, Neurolmage, vol. 7, no. 3, pp. 185-198, 1998.
8. G. H. GloverDeconvolution of impulse response in event-related BOLD fMRI, Neurolmage, vol. 9, no. 4, pp. 416-429, 1999.
9. K. J. Friston, P. Jezzard, and R. TurnerThe analysis of functional MRI time-series, Human Brain Mapping, vol. I, pp. 153-174, 1994.
10. N. Lange and S. L. ZegerNon-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging, J. Roy. Statistical Soc., ser. C, Appl. Stat., vol. 46, no. 1, pp. 1-30, 1997.
11. J. C. Rajapakse, F. Kruggel, J. M. Maisog, and D. Y. von CramonModeling hemodynamic response for analysis of functional MRI time-series, Human Brain Mapping, vol. 6, pp. 283-300, 1998.
12. J. Kershaw, B. A. Ardekani, and I. KannoApplication of Bayesian inference to fMRI data analysis, IEEE Trans. Med. Imag., vol. 18, pp. 1138-1153, Dec. 1999.
13. M. S. CohenParametric analysis of fMRI data using linear systems methods, Neurolmage, vol. 6, no. 2, pp. 93-103, Aug. 1997.
14. F. A. Nielsen, L. K. Hansen, P. Toft, C. Goutte, N. Lange, S. C. Strother, N. M0rch, C. Svarer, R. Savoy, B. Rosen, E. Rostrup, and B. PeterComparison of two convolution models for fMRI time series, in Friberg et al. [37], L. Friberg, A. Gjedde, S. Holm, N. A. Lassen, and M. Nowak, Eds. New York: Academic, May 1997, pt. 2 of 4 in Neurolmage, vol. 5, p. S473.
15. R. S. Menon, S. Ogawa, X. Hu, J. P. Strupp, P. Anderson, and K. UgurbilBOLD based functional MRI at 4 Tesla includes a capillary bed contribution: Echo-planar imaging correlates with previous optical imaging using intrinsic signals, Magn. Reson. Med, vol. 33, no. 3, pp. 453-459, 1995.
16. E. Yacoub and X. HuDetection of the early negative response in fMRI at 1.5 tesla, Magn. Reson. Med, vol. 41, no. 6, pp. 1088-1092, 1999.
17. G. Krüger, A. Kleinschmidt, and J. FrahmDynamic MRI sensitized to cerebral blood oxygénation and flow during sustained activation of human visual cortex, Magn. Reson. Med, vol. 35, no. 6, pp. 797-800, 1996.
18. C. Goutte, L. K. Hansen, M. G. Liptrot, and E. RostrupFeature space clustering for fMRI meta-analysis, IMM, Tech. Rep. IMM-REP-1999-13, 1999.
19. C. E. RasmussenEvaluation of Gaussian processes and other methods for nonlinear regression, Ph.D. dissertation, Dept. Comput. Sei., Univ. Toronto, Toronto, Canada, 1996.
20. C. K. I. WilliamsPrediction with Gaussian processes: From linear regression to linear prediction and beyond, in Learning and Inference in Graphical Models, M. I. Jordan, Ed. Norwell, MA: Kluwer, 1998.
21. P. A. Bandettini, A. Jesmanowicz, E. C. Wong, and J. S. HydeProcessing strategies for time-course data sets in functional MRI of the human brain, Magn. Reson. Med, vol. 30, no. 2, pp. 161-173, August 1993.
22. G. E. P. Box and G. C. Tiao, Bayesian Inference in Statistical Analysis. New York: Wiley, 1992.
23. M. J. SchervishP-values: What they are and what they are not, Amer. Statistician, vol. 50, no. 3, pp. 203-206, Aug. 1996.
24. M. Lavine and M. J. SchervishBayes factors: What they are and what they are not, Amer. Statistician, vol. 53, no. 2, pp. 119-122, May 1999.
25. A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
26. D. J. C. MacKayIntroduction to Monte Carlo methods, in Learning in Graphical Models, ser. NATO SCIENCE: D Behavioral and Social Sciences, M. I. Jordan, Ed. Dordrecht, The Netherlands: Kluwer Academic, 1998, vol. 89.
27. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 1992.
28. D. MacKayA practical Bayesian framework for backprop networks, Neural Computation, vol. 4, pp. 448-472, 1992.
29. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
30. C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. HansenOn clustering fMRI time series, Neurolmage, vol. 9, no. 3, pp. 298-310, 1999.
31. X. Hu, T. H. Le, and K. UgurbilEvaluation of the early response in fMRI in individual subjects using short stimulus duration, Magn. Reson. Med, vol. 37, no. 6, pp. 877-884, 1997.
32. E. Yacoub, T. H. Le, K. Ugurbil, and X. HuFurther evaluation of the initial negative response in functional magnetic resonance imaging, Magn. Reson. Med, vol. 41, no. 3, pp. 436-441, 1999.
33. G. M. Hathout, B. Varjavand, and R. K. GopiThe early response in fMRI: A modeling approach, Magn. Reson. Med, vol. 41, no. 3, pp. 550-554, 1999.
34. P. A. d. E. R. Højen-Sørensen, L. K. Hansen, and C. E. RasmussenBayesian modeling of fMRI time series, in Advances in Neural Information Processing Systems 12: Proceedings of the 1999 Conference, S. A. Solla, T. K. Leen, and K.-R. Müller, Eds. Cambridge, MA: MIT Press, 2000, pp. 754-760.
35. R. M. Neal, Bayesian Learning for Neural Networks. New York: Springer, 1996, vol. 118 of Lecture Notes in Statistics.
36. S. Chib and E. GreenbergUnderstanding the Metropolis-Hastings algorithm, The American Statistician, vol. 49, no. 4, pp. 327-335, 1995.
37. L. Friberg, A. Gjedde, S. Holm, N. A. Lassen, and M. Nowak,., Proceedings of the Third International Conference on Functional Mapping of the Human Brain. New York: Academic, May 1997, part 2 of 4 in Neurolmage.