Abstract
Comorbidity with anxiety disorders is a relatively common occurrence in major depressive disorder. However, there are no objective, neurological markers which can be used to identify depressive disorder with and without anxiety disorders. The aim of this study was to examine the diagnostic value of structural MRI to distinguish depressive patients with and without ss using support vector machine. In this paper, we applied voxel-based morphometry of gray matter volume (GMV), then choose discriminative features to classify different group using linear support vector machine (SVM) classifier. The experimental results showed that specific structural brain regions may be a potential biomarkers for disease diagnosis.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113 (2007)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 27 (2011)
Chen, Y.-W., Lin, C.-J.: Combining SVMs with various feature selection strategies. Feature Extraction, pp. 315–324. Springer (2006)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Good, C.D., Johnsrude, I., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S.: Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage 14, 685–700 (2001)
Inkster, B., Rao, A.W., Ridler, K., Nichols, T.E., Saemann, P.G., Auer, D.P., Holsboer, F., Tozzi, F., Muglia, P., Merlo-Pich, E., Matthews, P.M.: Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms. J. Neuroimaging 21, 375–382 (2011)
Van Tol, M.J., van der Wee, N.J., van den Heuvel, O.A., Nielen, M.M., Demenescu, L.R., Aleman, A., Renken, R., van Buchem, M.A., Zitman, F.G., Veltman, D.J.: Regional brain volume in depression and anxiety disorders. Arch. Gen. Psychiatry. 67, 1002–1011 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chi, M. et al. (2014). Using Support Vector Machine to Identify Imaging Biomarkers of Major Depressive Disorder and Anxious Depression. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-45049-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45048-2
Online ISBN: 978-3-662-45049-9
eBook Packages: Computer ScienceComputer Science (R0)