Abstract
Recent theoretical and experimental work in imaging neuroscience reveals that activations inferred from functional MRI data have sparse structure. We view sparse representation as a problem in Bayesian inference, following a machine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. Experimental results with benchmark datasets show that the proposed algorithm outperforms standard tools for model-free decompositions such as independent component analysis.
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
Worsley, K., Friston, K.: Analysis of fMRI time series revisited—again. NeuroImage 2, 173–181 (1995)
Beckmann, C., Smith, S.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imag. 23, 137–152 (2004)
McKeown, M., Sejnowski, T.: Independent component analysis of fMRI data: examining the assumptions. Hum. Brain Mapp. 6, 5–6, 368–372 (1998)
Daubechies, I., Roussos, E., Takerkart, S., Benharrosh, M., Golden, C., D’Ardenne, K., Richter, W., Cohen, J., Haxby, J.: Independent component analysis for brain fMRI does not select for independence. PNAS 106(26), 10415–10422 (2009)
Carroll, M., Cecchi, G., Rish, I., Garg, R., Rao, A.: Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44(1), 112–122 (2009)
Li, Y., Cichocki, A., Amari, S.-I., Shishkin, S., Cao, J., Gu, F.: Sparse representation and its applications in blind source separation. In: Proceedings of the Annual Conference on Neural Information Processing Systems 17 (2003)
Turkheimer, F., Brett, M., Aston, J., Leff, A., Sargent, P., Wise, R., Grasby, P., Cunningham, V.: Statistical modelling of PET images in wavelet space. Journal of Cerebral Blood Flow and Metabolism 20, 1610–1618 (2001)
Bullmore, E., Fadili, J., Breakspear, M., Salvador, R., Suckling, J., Brammer, M.: Wavelets and statistical analysis of functional magnetic resonance images of the human brain. Statistical Methods in Medical Research 12, 375–399 (2003)
MacKay, D.: Probable Networks and Plausible Predictions - A Review of Practical Bayesian Methods for Supervised Neural Networks. Network: Computation in Neural Systems 6, 469–505 (1995)
Attias, H.: A Variational Bayesian Framework for Graphical Models. In: Proceedings of Advances in Neural Information Processing Systems, vol. 12 (2000)
Penny, W., Roberts, S.: Variational Bayes for 1-dimensional Mixture Models. Techn. Rep. PARG–00–2, Dept. of Engineering Science, University of Oxford (2000)
Smith, S., Jenkinson, M., Woolrich, M., Beckmann, C., Behrens, T., Johansen-Berg, H., Bannister, P., De Luca, M., Drobnjak, I., Flitney, D., Niazy, R., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J., Matthews, P.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(S1), 208–219 (2004)
Choudrey, R., Penny, W., Roberts, S.: An Ensemble Learning Approach to Independent Component Analysis. In: Proceedings of Neural Networks for Signal Processing (2000)
Roussos, E., Roberts, S., Daubechies, I.: Variational Bayesian Learning for Wavelet Independent Component Analysis. In: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 25 (2005)
Groves, A.: Bayesian Learning Methods for Modelling Functional MRI. D.Phil. Thesis, Department of Clinical Neurology, University of Oxford (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roussos, E., Roberts, S., Daubechies, I. (2012). Variational Bayesian Learning of Sparse Representations and Its Application in Functional Neuroimaging. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-34713-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34712-2
Online ISBN: 978-3-642-34713-9
eBook Packages: Computer ScienceComputer Science (R0)