Abstract
Discovering brain mechanisms underlying pain perception remains a challenging neuroscientific problem with important practical applications, such as developing better treatments for chronic pain. Herein, we focus on statistical analysis of functional MRI (fMRI) data associated with pain stimuli. While the traditional mass-univariate GLM [8] analysis of pain-related brain activation can miss potentially informative voxel interaction patterns, our approach relies instead on multivariate predictive modeling methods such as sparse regression (LASSO [17] and, more generally, Elastic Net (EN) ([18]) that can learn accurate predictive models of pain and simultaneously discover brain activity patterns (relatively small subsets of voxels) allowing for such predictions. Moreover, we investigate the effect of temporal (time-lagged) information, often ignored in traditional fMRI studies, on the predictive accuracy and on the selection of brain areas relevant to pain perception. We demonstrate that (1) Elastic Net regression can be highly predictive of pain perception, by far outperforming ordinary least-squares (OLS) linear regression; (2) temporal information is very important for pain perception modeling and can significantly increase the prediction accuracy; (3) moreover, regression models that incorporate temporal information discover brain activation patterns undetected by non-temporal models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Apkarian, A.V., Bushnell, M.C., Treede, R.D., Zubieta, J.K.: Human brain mechanisms of pain perception and regulation in health and disease. Eur. J. Pain (9), 463–484 (2005)
Baliki, M.N., Geha, P.Y., Apkarian, A.V.: Parsing pain perception between nociceptive representation and magnitude estimation. Journal of Neurophysiology (101), 875–887 (2009)
Baliki, M.N., Geha, P.Y., Apkarian, A.V., Chialvo, D.R.: Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics. J. Neurosci. (28), 1398–1403 (2008)
Battle, A., Chechik, G., Koller, D.: Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19, pp. 121–128. MIT Press, Cambridge (2007)
Buchel, C., Bornhovd, K., Quante, M., Glauche, V., Bromm, B., Weiller, C.: Dissociable neural responses related to pain intensity, stimulus intensity, and stimulus awareness within the anterior cingulate cortex: a parametric single-trial laser functional magnetic resonance imaging study. J. Neurosci. (22), 970–976 (2002)
Carroll, M.K., Cecchi, G.A., Rish, I., Garg, R., Rao, A.R.: Prediction and Interpretation of Distributed Neural Activity with Sparse Models. Neuroimage 44(1), 112–122 (2009)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Statist. 32(1), 407–499 (2004)
Friston, K.J., et al.: Statistical parametric maps in functional imaging - a general linear approach. Human Brain Mapping 2, 189–210 (1995)
Pereira, F., Gordon, G.: The Support Vector Decomposition Machine. In: ICML 2006, pp. 689–696 (2006)
Frank, I., Friedman, J.: A statistical view of some chemometrics regression tools. Technometrics 35(2), 109–148 (1993)
Fu, W.: Penalized regression: the bridge versus the lasso. J. Comput. Graph. Statist. 7(2), 397–416 (1998)
Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex. Science 293(5539), 2425–2430 (2001)
Hoerl, A., Kennard, R.: Ridge regression. Encyclopedia of Statistical Sciences 8(2), 129–136 (1988)
Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to Decode Cognitive States from Brain Images. Machine Learning 57, 145–175 (2004)
Price, D.D.: Psychological and neural mechanisms of the affective dimension of pain. Science (288), 1769–1772 (2000)
Sjöstrand, K.: Matlab implementation of LASSO, LARS, the elastic net and SPCA, Version 2.0. (June 2005)
Tibshirani, R.: Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B 58(1), 267–288 (1996)
Zou, H., Hastie, T.: Regularization and variable selection via the Elastic Net. Journal of the Royal Statistical Society, Series B 67(2), 301–320 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rish, I., Cecchi, G.A., Baliki, M.N., Apkarian, A.V. (2010). Sparse Regression Models of Pain Perception. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds) Brain Informatics. BI 2010. Lecture Notes in Computer Science(), vol 6334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15314-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-15314-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15313-6
Online ISBN: 978-3-642-15314-3
eBook Packages: Computer ScienceComputer Science (R0)