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G.-Z. Yang et al. (Eds.): MICCAI 2009, Part II, LNCS 5762, pp. 490497, 2009. Springer-Verlag Berlin Heidelberg 2009
 

Summary: G.-Z. Yang et al. (Eds.): MICCAI 2009, Part II, LNCS 5762, pp. 490497, 2009.
Springer-Verlag Berlin Heidelberg 2009
Functional Segmentation of fMRI Data Using Adaptive
Non-negative Sparse PCA (ANSPCA)
Bernard Ng1
, Rafeef Abugharbieh1
, and Martin J. McKeown2
1
Biomedical Signal and Image Computing Lab, Department of Electrical Engineering
2
Department of Medicine (Neurology), Pacific Parkinson's Research Center
The University of British Columbia, Vancouver, BC, Canada
bernardn@ece.ubc.ca, rafeef@ece.ubc.ca,
mmckeown@interchange.ubc.ca
Abstract. We propose a novel method for functional segmentation of fMRI
data that incorporates multiple functional attributes such as activation effects
and functional connectivity, under a single framework. Similar to PCA, our
method exploits the structure of the correlation matrix but with neighborhood
information adaptively integrated to encourage detection of spatially contiguous
clusters yet without falsely pooling non-active voxels near the functional

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences