Abstract
Functional magnetic resonance imaging (fMRI) has provided an invaluable method of investing real time neuron activities. Statistical tools have been developed to recognise the mental state from a batch of fMRI observations over a period.
However, an interesting question is whether it is possible to estimate the real time mental states at each moment during the fMRI observation. In this paper, we address this problem by building a probabilistic model of the brain activity. We model the tempo-spatial relations among the hidden high-level mental states and observable low-level neuron activities. We verify our model by experiments on practical fMRI data. The model also implies interesting clues on the task-responsible regions in the brain.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W.: Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences 87, 9868–9872 (1990)
Friston, K.J., Holmes, A.P., Poline, J.B., Gransby, P.J., Williams, S.C.R., Frackowiak, R.S.J., Turner, R.: Analysis of fMRI time series revisited. NeuroImage 2(1), 45–43 (1995)
Lange, N., Zeger, S.L.: Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Applied Statistics 46(1), 1–29 (1997)
Wang, Y., Rajapakse, J.C.: Contextual modeling of functional MR images with conditional random fields. IEEE Transactions on Medical Imaging 25(6), 804–812 (2006)
Quiriós, A., Diez, R.M., Wilson, S.P.: Bayesian spatiotemporal model of fMRI data using transfer functions. NeuroImage (2010)
Goutte, C., Toft, P., Rostrup, E., Nielsen, F., Hansen, L.K.: On clustering fMRI time series. NeuroImage 9(3), 298–310 (1999)
Hansen, L.K., Larsen, J., Nielsen, F.A., Strother, S.C., Rostrup, E., Savoy, R., Lange, N., Sidtis, J., Svarer, C., Paulson, O.B.: Generalizable patterns in neuroimaging: how many principal components? NeuroImage 9(5), 534–544 (1999)
Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X.: Learning to decode cognitive states from brain images. Machine Learning 57, 145–175 (2004)
Sutton, C., Mccallum, A.: An Introduction to Conditional Random Fields for Relational Learning. MIT Press (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Murray, L.J., Ranganath, C.: The dorsolateral prefrontal cortex contributes to successful relational memory encoding. Journal of Neuroscience 27, 5515–5522 (2007)
Kolb, B., Whishaw, I.: Fundamentals of Human Neuropsychology. Worth Publishers (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, J., Tao, D. (2011). A Probabilistic Model for Discovering High Level Brain Activities from fMRI. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)