BRAIN ACTIVITY DETECTION - Statistical Analysis of fMRI Data
Alicia Quirós Carretero, Raquel Montes Diez
2009
Abstract
We are concerned with modelling and analysing fMRI data. An fMRI experiment is a series of images obtained over time under two different conditions, in which regions of activity are detected by observing differences in blood magnetism due to hemodynamic response. In this paper we propose a spatiotemporal model for detecting brain activity in fMRI. The model makes no assumptions about the shape or form of activated areas, except that they emit higher signals in response to a stimulus than non-activated areas do, and that they form connected regions. The Bayesian spatial prior distributions provide a framework for detecting active regions much as a neurologist might; based on posterior evidence over a wide range of spatial scales, simultaneously considering the level of the voxel magnitudes together with the size of the activated area. A single spatiotemporal Bayesian model allows more information to be obtained about the corresponding magnetic resonance study. Despite higher computational cost, a spatiotemporal model improves the inference ability since it takes into account the uncertainty in both the spatial and the temporal dimension. A simulated study is used to test the model applicability and sensitivity.
References
- Beckmann, C. F. and Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23(2):137-152.
- Bowman, F. D. (2007). Spatiotemporal models for region of interest analysis of functional neuroimaging data. Journal of the American Statistical Association, 102(478):442-453.
- Bowman, F. D., Caffo, B. S., Bassett, S. S., and Kilts, C. (2008). A bayesian hierarchical framework for spatial modeling of fmri data. NeuroImage, 39(1):146-156.
- Calhoun, V. D., Adali, T., McGinty, V. B., Pekar, J. J., Watson, T. D., and Pearlson, G. D. (2001). fmri activation in a visual-perception task: Network of areas detected using the general linear model and independent components analysis. NeuroImage, 14(5):1080-1088.
- Esposito, F., Seifritz, E., Formisano, E., Morrone, R., Scarabino, T., Tedeschi, G., Cirillo, S., Goebel, R., and Di Salle, F. (2003). Real-time independent component analysis of fmri time-series. NeuroImage, 20(4):2209-2224.
- Frackowiak, R. S. J., Friston, K. J., Frith, C. D., Dolan, R. J., Price, C. J., Zeki, S., Ashburner, J. T., and Penny, W. D. (2004). Human Brain Function. Academic Press.
- Friston, K. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2:56-78.
- Friston, K. J., Ashburner, J., Kiebel, S., Nichols, T., and Penny, W. (2006). Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier, London.
- Friston, K. J., Holmes, A. P., Poline, J., Gransby, P. J., Williams, S. C. R., Frackowiak, J., R. S., and Turner, R. (1995). Analysis of fmri time - series revisited. NeuroImage, 2(1):45-53.
- Friston, K. J., Jezzard, P., and Turner, R. (1994). Analysis of functional mri time-series. Human Brain Mapping, 1(2):153-171.
- Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC.
- Gossl, C., Auer, D. P., and Fahrmeir, L. (1999). A bayesian approach for spatial connectivity in fmri. Poster No. 483 Biometrics, 57:554-562.
- Gossl, C., Auer, D. P., and Fahrmeir, L. (2001). Bayesian spatiotemporal inference in functional magnetic resonance imaging. Biometrics, 57(2):554-562.
- Goutte, C., Toft, P., Rostrup, E., Nielsen, F. A., and Hansen, L. K. (1999). On clustering fmri time series. NeuroImage, 9(3):298-310.
- Harrison, L. M., Penny, W., Ashburner, J., Trujillo-Barreto, N., and Friston, K. J. (2007). Diffusion-based spatial priors for imaging. NeuroImage, 38(4):677-695.
- Hartvig, N. and Jensen, J. (2000). Spatial mixture modelling of fmri data. Human Brain Mapping, 11(4):233-248.
- Holmes, A. (1995). Statistical Issues in Functional Brain Mapping. Chapter 5 - An Empirical Bayesian Approach. PhD thesis.
- Huang, H. (1999). PACS: basic principles and applications. Wiley-Liss, New York.
- Jezzard, P., Matthews, P. M., and Smith, S. M. (2001). Functional MRI: An introduction to methods. Oxford University Press, New York.
- Katanoda, K., Matsuda, Y., and Sugishita, M. (2002). A spatio-temporal regression model for the analysis of functional mri data. NeuroImage, 17(3):1415-1428.
- Kornak, J. (2000). Bayesian Spatial Inference from Haemodynamic Response Parameters in Functional Magnetic Resonance Imaging. PhD thesis, University of Nottingham.
- Lange, N. and Zeger, S. L. (1997). Non - linear fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Applied Statistics, 46(1):1-29.
- McKeown, M. J., Hansen, L. K., , and Sejnowsk, T. J. (2003). Independent component analysis of functional mri: what is signal and what is noise? Current Opinion in Neurobiology, 13(5):620-629.
- Ogawa, S., Lee, T. M., Kay, A. R., and Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87(24):9868-9872.
- Penny, W. D., Trujilllo-Barreto, N. J., and Friston, K. J. (2005). Bayesian fmri time series analysis with spatial priors. NeuroImage, 24(2):350-362.
- Porill, J., Stone, J. V., Mayhew, J. E. W., Berwick, J., and Coffey, P. (1999). Spatio - temporal data analysis using weak linear models. In Mardia, K., otro, and otro, editors, Spatial Temporal Modelling and its Applications, pages 17-20.
- Quirós, A., Montes, R., and Hernandez, J. (2006). A fully bayesian two - stage model for detecting brain activity in fmri. Lecture Notes in Computer Science, 4345:334-345.
- Rue, H. and Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications, volume 104 of Monographs on Statistics and Applied Probability. Chapman & Hall/CRC, London.
- Sjöstrand, K., Lund, T. E., Madsen, K. H., and Larsen, R. (2006). Sparse PCA, a new method for unsupervised analyses of fmri data. In Proc. International Society of Magnetic Resonance In Medicine - ISMRM 2006, Seattle, Washington, USA, Berkeley, CA, USA. ISMRM.
- Winkler, G. (2003). Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introduction. Springer, cop.
- Woolrich, M. W., Behrens, T. E. J., and Smith, S. M. (2004a). Constrained linear basis sets for hrf modelling using variational bayes. NeuroImage, 21(4):1748-1761.
- Woolrich, M. W., Jenkinson, M., Brady, J. M., and Smith, S. M. (2004b). Fully bayesian spatio - temporal modeling of fmri data. IEEE Transactions on Medical Imaging, 23(2):213-231.
Paper Citation
in Harvard Style
Quirós Carretero A. and Montes Diez R. (2009). BRAIN ACTIVITY DETECTION - Statistical Analysis of fMRI Data . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 434-439. DOI: 10.5220/0001781204340439
in Bibtex Style
@conference{biosignals09,
author={Alicia Quirós Carretero and Raquel Montes Diez},
title={BRAIN ACTIVITY DETECTION - Statistical Analysis of fMRI Data},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={434-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001781204340439},
isbn={978-989-8111-65-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - BRAIN ACTIVITY DETECTION - Statistical Analysis of fMRI Data
SN - 978-989-8111-65-4
AU - Quirós Carretero A.
AU - Montes Diez R.
PY - 2009
SP - 434
EP - 439
DO - 10.5220/0001781204340439