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Semi-supervised Analysis of Human fMRI Data




Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, CCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, CCA may suffer from small sample effects. We propose to use semisupervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of CCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.

Author(s): Shelton, JA. and Blaschko, MB. and Lampert, CH. and Bartels, A.
Journal: Berlin Brain Computer Interface Workshop on Advances in Neurotechnology
Volume: 2009
Pages: 1
Year: 2009
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Semi-supervised Analysis of Human fMRI Data},
  author = {Shelton, JA. and Blaschko, MB. and Lampert, CH. and Bartels, A.},
  journal = {Berlin Brain Computer Interface Workshop on Advances in Neurotechnology},
  volume = {2009},
  pages = {1},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2009},
  month_numeric = {7}