Reading minds using classification algorithms on fMRI data
Resumo
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive method to obtain brain images that indirectly shows neuronal activation. With fMRI scans, we are able to measure areas of the brain that are active in time during extension of the exam, which are often transformed into a time-sequence of images. These images are then analyzed by human experts to infer information of interest. Recent work has used machine learning algorithms to extract more complex information from fMRI scans. In this paper we propose to use a classification based algorithm to differentiate, at each time point during the scan, whether a single patient is performing a task or not. We process the data to generate examples when the patient is performing a task or resting, and experiment different parameters for the classification algorithm to achieve a high success rate.Referências
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Woodard, J., Seidenberg, M., Nielson, K., Antuono, P., Guidotti, L., Durgerian, S., Zhang, Q., Lancaster, M., Hantke, N., Butts, A., et al. (2009). Semantic memory activation in amnestic mild cognitive impairment. Brain, 132(8):2068–2078.
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Biswal, B., Zerrin Yetkin, F., Haughton, V. M., and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic resonance in medicine, 34(4):537–541.
Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27. Software available at [link].
Cox, R. W. (1996). Afni: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research, 29(3):162–173.
Hoeft, F., McCandliss, B. D., Black, J. M., Gantman, A., Zakerani, N., Hulme, C., Lyytinen, H., Whitfield-Gabrieli, S., Glover, G. H., Reiss, A. L., et al. (2011). Neural systems predicting long-term outcome in dyslexia. Proceedings of the National Academy of Sciences, 108(1):361–366.
Huettel, S. A., Song, A. W., and McCarthy, G. (2004). Functional magnetic resonance imaging, volume 1. Sinauer Associates Sunderland.
Just, M. A., Keller, T. A., Malave, V. L., Kana, R. K., and Varma, S. (2012). Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity. Neuroscience & Biobehavioral Reviews, 36(4):1292–1313.
Keerthi, S. S. and Lin, C.-J. (2003). Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation, 15(7):1667–1689.
Mitchell, T. M. (1997). Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 45.
Mitchell, T. M., Hutchinson, R., Just, M. A., Niculescu, R. S., Pereira, F., and Wang, X. (2003). Classifying instantaneous cognitive states from fmri data. In AMIA Annual Symposium Proceedings, volume 2003, page 465. American Medical Informatics Association.
Nelder, J. A. and Mead, R. (1965). A simplex method for function minimization. Computer journal, 7(4):308–313.
Pereira, F., Mitchell, T., and Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1, Supplement 1):S199–S209. Mathematics in Brain Imaging.
Powell, M. J. (1987). Radial basis functions for multivariable interpolation: a review. In Algorithms for approximation, pages 143–167. Clarendon Press.
Russell, S. J. and Norvig, P. (2010). Artificial intelligence: a modern approach, volume 2. Prentice hall.
Vapnik, V. (2000). The nature of statistical learning theory. springer.
Wolf, M. and Bowers, P. G. (1999). The double-deficit hypothesis for the developmental dyslexias. Journal of Educational Pscyhology, 91(3):415–438.
Woodard, J., Seidenberg, M., Nielson, K., Antuono, P., Guidotti, L., Durgerian, S., Zhang, Q., Lancaster, M., Hantke, N., Butts, A., et al. (2009). Semantic memory activation in amnestic mild cognitive impairment. Brain, 132(8):2068–2078.
Woodard, J. L. and Sugarman, M. A. (2012). Functional magnetic resonance imaging in aging and dementia: Detection of age-related cognitive changes and prediction of cognitive decline. In Behavioral Neurobiology of Aging, pages 113–136. Springer.
Publicado
28/05/2014
Como Citar
FROEHLICH, Caroline; FRANCO, Alexandre R.; MENEGUZZI, Felipe.
Reading minds using classification algorithms on fMRI data. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 8. , 2014, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 69-77.
ISSN 2326-5434.
