Abstract
This paper presents a new and general nonlinear framework for fMRI data analysis based on statistical learning methodology: support vector machines. Unlike most current methods which assume a linear model for simplicity, the estimation and analysis of fMRI signal within the proposed framework is nonlinear, which matches recent findings on the dynamics underlying neural activity and hemodynamic physiology. The approach utilizes spatio-temporal support vector regression (SVR), within which the intrinsic spatio-temporal autocorrelations in fMRI data are reflected. The novel formulation of the problem allows merging model-driven with data-driven methods, and therefore unifies these two currently separate modes of fMRI analysis. In addition, multiresolution signal analysis is achieved and developed. Other advantages of the approach are: avoidance of interpolation after motion estimation, embedded removal of low-frequency noise components, and easy incorporation of multi-run, multi-subject, and multi-task studies into the framework.
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References
Backfriender, W., Baumgartner, R., Stamal, M., Moser, E., Bergmann, H.: Quantification of intensity variations in functional MR images using rotated principal components. Physics in Medicine and Biology 41, 1425–1438 (1996)
Birn, R.M., Saad, Z.S., Bandettini, P.A.: Spatial heterogeneity of the nonlinear dynamics in the fMRI BOLD response. Neuroimage 14, 817–826 (2001)
Boulanouar, K., Roux, F., Celsis, P.: Modeling brain hemodynamic response in functional MRI using vector support method. In: Cognitive Neuroscience Society Annual Meeting (2001) (abstract)
Boynton, G.M., Engel, S.A., Glover, G.H., Heeger, D.J.: Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neuroscience 16, 4207–4221 (1996)
Collobert, R., Bengio, S.: SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research 1, 143–160 (2001)
Constable, R.T., Skudlarski, P., Gore, J.C.: An ROC approach for evaluating functional brain MR imaging and postprocessing protocols. Magnetic Resonance in Medicine 34, 57–64 (1995)
Descombes, X., Kruggel, F., von Cramon, D.Y.: fMRI signal restoration using a spatiotemporal Markov random field preserving transitions. Neuroimage 8, 340–349 (1998)
Friston, K.J., et al.: Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping 2, 189–210 (1995)
Friston, K.J., Holmes, A.P., Poline, J.-B., Grasby, P.J., Williams, S.C.R., Frackowiak, R.S.J.: Analysis of fMRI time-series revisited. Neuroimage 2, 45–53 (1995)
Golland, P., et al.: Discriminative analysis for image-based studies. In: Intl. Conf. on Medical Image Computing and Computer-Assisted Intervention, pp. 508–515 (2002)
Gretton, A., Doucer, A., Herbrich, R., Rayner, P.J.W., Scholkopf, B.: Support vector regression for black-box system identification. In: IEEE Workshop on Statistical Signal Processing, pp. 341–344 (2001)
Grootoonk, S., Hutton, C., Ashburner, J., Howseman, A.M., Josephs, O., Rees, G., Friston, K.J., Turner, R.: Characterization and correction of interpolation effects in the realignment of fMRI time series. Neuroimage 11, 49–57 (2000)
Hartvig, N.V., Jensen, J.L.: Spatial mixture modeling of fMRI data. Human Brain Mapping 11, 233–248 (2000)
Johnson, R.A., Bhattacharyya, G.K.: Statistics: Principles and Methods. John Wiley & Sons, Inc., Chichester (2001)
Katanoda, K., Matsuda, Y., Sugishita, M.: A spatio-temporal regression model for the analysis of functional MRI data. Neuroimage 17, 1415–1428 (2002)
Laird, A.R., Rogers, B.P., Meyerand, M.E.: Investigating the nonlinearity of fMRI activation data. In: Proc. Second Joint EMBS / BMES Conference, pp. 23–26 (2002)
Lang, N.: Statistical procedures for functional MRI. In: Moonen, C., Bandettini, P. (eds.) Functional MRI, pp. 301–355. Springer, Heidelberg (1999)
Li, Y., Gong, S., Liddell, H.: Support vector regression and classification based multiview face detection and recognition. In: Proc. Fourth IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 300–305 (2000)
Marchini, J.L., Ripley, B.D.: A new statistical approach to detecting significant activation in functional MRI. Neuroimage 12, 366–380 (2000)
McKeown, M., Makeig, S., Brown, G., Jung, T., Kindermann, S., Bell, A., Sejnowski, T.: Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping 6, 160–188 (1998)
Miller, K.L., Luh, W.M., Lie, T.L., Martinez, A., Obata, T., Wong, E.C., Frank, L.R., Buxton, R.B.: Nonlinear temporal dynamics of the cerebral blood flow response. Human Brain Mapping 13, 1–12 (2001)
Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear prediction of chaotic time series using support vector machines. In: Proc. IEEE Workshop on Neural Networks and Signal Processing, vol. VII, pp. 511–520 (1997)
Ogawa, S., Lee, T.M., Nayak, A.S., Glynn, P.: Oxygenation-sensitive contrast in magnetic resonance image of rodent brain of high magnetic fields. Magnetic Resonance in Medicine 14, 68–78 (1990)
Schultz, R.T., et al.: The role of the fusiform face area in social cognition: Implications for the pathobiology of autism. Phil. Trans. of the Royal Society, Series B 358, 415–427 (2003)
Skudlarski, P., Constable, R.T., Gore, J.C.: ROC analysis of statistical methods used in functional MRI: Individual subjects. Neuroimage 9, 311–329 (1999)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK (1998)
Vapnik, V.N.: Statistical Leaning Theory. John Wiley & Sons, New York (1998)
Zarahn, E., Aguirre, G.K., D’Esposito, M.: Empirical analyses of BOLD fMRI statistics. I. Spatially unsmoohed data collected under null-hypothesis conditions. Neuroimage 5, 179–197 (1997)
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Wang, Y.M., Schultz, R.T., Constable, R.T., Staib, L.H. (2003). Nonlinear Estimation and Modeling of fMRI Data Using Spatio-temporal Support Vector Regression. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_54
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DOI: https://doi.org/10.1007/978-3-540-45087-0_54
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