Decoding fMRI data with support vector machines and deep neural networks.

Journal: Journal of neuroscience methods
Published Date: Oct 31, 2023

Abstract

BACKGROUND: Multivoxel pattern analysis (MVPA) examines fMRI activation patterns associated with different cognitive conditions. Support vector machines (SVMs) are the predominant method in MVPA. While SVM is intuitive and easy to apply, it is mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs) are known to have the ability to approximate nonlinear relationships. Applications of CNN to fMRI data are beginning to appear with increasing frequency, but our understanding of the similarities and differences between CNN models and SVM models is limited.

Authors

  • Yun Liang
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United States.
  • Ke Bo
    J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
  • Sreenivasan Meyyappan
    J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
  • Mingzhou Ding
    J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.

Keywords

External Resources

Popular Topics
Recent Journals