Abstract
Cocaine dependence continues to devastate millions of human lives. According to the 2013 National Survey on Drug Use and Health, approximately 1.5 million Americans are currently addicted to cocaine. It is important to understand how cocaine addicts and non-addicted individuals differ in the functional organization of the brain. This work advances the identification of cocaine dependence based on fMRI classification and innovates by employing deep learning methods. Deep learning has proved its utility in machine learning community, mainly in computational vision and voice recognition. Recently, studies have successfully applied it to fMRI data for brain decoding and classification of pathologies, such as schizophrenia and Alzheimer’s disease. These fMRI data were relatively large, and the use of deep learning in small data sets still remains a challenge. In this study, we fill this gap by (i) using Deep Belief Networks and Deep Neural Network to classify cocaine dependents from fMRI, and (ii) presenting a novel stratification method for robust training and evaluation of a relatively small data set. Our results show that deep learning outperforms traditional techniques in most cases, and present a great potential for improvement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, Cambridge (2008)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Friston, K.J.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier/Academic Press, Amsterdam/Boston (2007)
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2(4), 189–210 (1995)
Fritzke, B., et al.: A growing neural gas network learns topologies. Adv. Neural Inf. Process. Syst. 7, 625–632 (1995)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Aistats, vol. 9, pp. 249–256 (2010)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
Haxby, J.V., Connolly, A.C., Guntupalli, J.S.: Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37, 435–456 (2014)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River (1998)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science (New York, N.Y.) 313(5786), 504–507 (2006)
Honorio, J.: Classification on brain functional magnetic resonance imaging: dimensionality, sample size, subject variability and noise. In: Chen, C. (ed.) Frontiers of Medical Imaging, pp. 266–290. World Scientific Publishing Company Pte Limited, Singapore (2014)
Ide, J., Shenoy, P., Yu, A., Li, C.: Bayesian prediction and evaluation in the anterior cingulate cortex. J. Neurosci. 33(5), 2039–2047 (2013)
Jones, N.: Computer science: the learning machines. Nature 505, 146–148 (2014)
Koyamada, S., Shikauchi, Y., Nakae, K., Koyama, M., Ishii, S.: Deep learning of fMRI big data: a novel approach to subject-transfer decoding. arXiv preprint arXiv:1502.00093 (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Li, C.S.R., Huang, C., Constable, R.T., Sinha, R.: Imaging response inhibition in a stop-signal task: neural correlates independent of signal monitoring and post-response processing. J. Neurosci. 26(1), 186–192 (2006)
Luo, X., Zhang, S., Hu, S., Bednarski, S.R., Erdman, E., Farr, O.M., Hong, K.I., Sinha, R., Mazure, C.M., shan, R., Li, C.: Error processing and gender-shared and -specific neural predictors of relapse in cocaine dependence. Brain 136(4), 1231–1244 (2013)
Markoff, J.: Scientists See Promise in Deep-Learning Programs. New York Times, Manhattan (2012)
Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10(9), 424–430 (2017). http://dx.doi.org/10.1016/j.tics.2006.07.005
de Oliveira, F.A., Nobre, C.N., Zárate, L.E.: Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index-case study of PETR4, Petrobras, Brazil. Expert Syst. Appl. 40(18), 7596–7606 (2013)
Pahlavan, R., Omid, M., Akram, A.: Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy 37(1), 171–176 (2012)
Plis, S.M., Hjelm, D.R., Salakhutdinov, R., Allen, E.A., Bockholt, H.J., Long, J.D., Johnson, H.J., Paulsen, J.S., Turner, J.A., Calhoun, V.D.: Deep learning for neuroimaging: a validation study. Front. Neurosci. 8(August), 1–11 (2014)
Roebroeck, A., Formisano, E., Goebel, R.: Mapping directed influence over the brain using granger causality and fMRI. Neuroimage 25(1), 230–242 (2005)
Smith, S.M., Nichols, T.E., Vidaurre, D., Winkler, A.M., Behrens, T.E.J., Glasser, M.F., Ugurbil, K., Barch, D.M., Van Essen, D.C., Miller, K.L.: A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18(11), 1565–1567 (2015)
Tomasi, D., Volkow, N.D., Wang, R., Carrillo, J.H., Maloney, T., Alia-Klein, N., Woicik, P.A., Telang, F., Goldstein, R.Z.: Disrupted functional connectivity with dopaminergic midbrain in cocaine abusers. PLoS ONE 5(5), 1–10 (2010)
Zhang, S., Hu, S., Bednarski, S.R., Erdman, E., Li, C.S.: Error-related functional connectivity of the thalamus in cocaine dependence. Neuroimage Clin. 4, 585–592 (2014)
Acnowledgement
The authors would like to thank CAPES, CNPq (458777/2014-5), FAPESP (2016/02870-0, 2016/16291-2) and NIH (grant R01DA023248) for funding this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Santos, J.S., Savii, R.M., Ide, J.S., Li, CS.R., Quiles, M.G., Basgalupp, M.P. (2017). Classification of Cocaine Dependents from fMRI Data Using Cluster-Based Stratification and Deep Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-62392-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62391-7
Online ISBN: 978-3-319-62392-4
eBook Packages: Computer ScienceComputer Science (R0)