(A) data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion = 희소사전학습과 최소표현길이기준을 이용한 뇌기능자기공명영상의 데이터기반 Sparse GLM

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 526
  • Download : 0
A novel statistical analysis method for functional MRI to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA) is developed. Although ICA has been broadly applied to functional MRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studies showing that ICA does not guarantee independence of simultaneously occurring distinct activity patterns in the brain. Instead, sparsity of the signal has been shown to be more promising. This coincides with biological findings such as sparse coding in V1 simple cells, electrophysiological experiment results in the human medial temporal lobe, and etc. The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals. A compressed sensing based data-driven sparse generalized linear model is proposed that enables estimation of spatially adaptive design matrix as well as sparse signal components that represent synchronous, functionally organized and integrated neural hemodynamics. Furthermore, an MDL based model order selection rule is shown to be essential in selecting unknown sparsity level for sparse dictionary learning. Multi-level analysis of fMRI data using data-driven sparse GLM is also investigated. Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.
Advisors
Ye, Jong-Chulresearcher예종철researcher
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2011
Identifier
467644/325007  / 020093343
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2011.2, [ vi, 39 p ]

Keywords

data-driven fMRI analysis; K-SVD; sparse dictionary learning; Sparse generalized linear model; compressed sensing; 압축센싱; 데이터기반 fMRI 분석; K-SVD; 희소사전학습; 희소일반화선형모델

URI
http://hdl.handle.net/10203/27209
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=467644&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0