Share Email Print

Proceedings Paper

Sparse dictionary learning for resting-state fMRI analysis
Author(s): Kangjoo Lee; Paul Kyu Han; Jong Chul Ye
Format Member Price Non-Member Price
PDF $17.00 $21.00

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
Show Author Affiliations
Kangjoo Lee, 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)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?