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Sparse dictionary learning for resting-state fMRI analysisFormat | Member Price | Non-Member Price |
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Paper Abstract
Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens
in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted
in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the
application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing
based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have
features of complex networks according to graph theoretical analysis, we apply a graph model to represent a
sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially
adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The
proposed approach shows better performance compared to other conventional methods, such as independent
component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state
analysis.
Paper Details
Date Published: 27 September 2011
PDF: 7 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381X (27 September 2011); doi: 10.1117/12.894241
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
PDF: 7 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381X (27 September 2011); doi: 10.1117/12.894241
Show Author Affiliations
Kangjoo Lee, KAIST (Korea, Republic of)
Paul Kyu Han, KAIST (Korea, Republic of)
Paul Kyu Han, KAIST (Korea, Republic of)
Jong Chul Ye, KAIST (Korea, Republic of)
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
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