Brain functional mapping using spatially regularized support vector machines

Published

Conference Paper

© 2015 IEEE. Quantitative functional magnetic resonance imaging (fMRI) requires reliable mapping of brain function in task-or resting-state. In this work, a spatially regularized support vector machine (SVM)-based technique was proposed for brain functional mapping of individual subjects and at the group level. Unlike most SVM-based fMRI data analysis approaches that conduct supervised classifications of brain functional states or disorders, the proposed technique performs a semi-supervised learning to provide a general mapping of brain function in task-or resting-state. The method can adapt to between-session and between-subject variations of fMRI data, and provide a reliable mapping of brain function. The proposed method was evaluated using synthetic and experimental data. A comparison with independent component analysis methods was also performed using the experimental data. Experimental results indicate that the proposed method can provide a reliable mapping of brain function and be used for different quantitative fMRI studies.

Full Text

Duke Authors

Cited Authors

  • Song, X; Panych, LP; Chen, NK

Published Date

  • February 11, 2016

Published In

  • 2015 Ieee Signal Processing in Medicine and Biology Symposium Proceedings

International Standard Book Number 13 (ISBN-13)

  • 9781509013500

Digital Object Identifier (DOI)

  • 10.1109/SPMB.2015.7405466

Citation Source

  • Scopus