Abstract
Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.
| Original language | English |
|---|---|
| Pages (from-to) | 220-231 |
| Number of pages | 12 |
| Journal | Pattern Recognition |
| Volume | 90 |
| DOIs | |
| Publication status | Published - 2019 Jun 1 |
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Keywords
- Brain functional network
- Graph Laplacian regularization
- Mild cognitive impairment (MCI)
- Sparse representation
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
Weighted graph regularized sparse brain network construction for MCI identification. / Yu, Renping; Qiao, Lishan; Chen, Mingming; Lee, Seong Whan; Fei, Xuan; Shen, Dinggang.
In: Pattern Recognition, Vol. 90, 01.06.2019, p. 220-231.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Weighted graph regularized sparse brain network construction for MCI identification
AU - Yu, Renping
AU - Qiao, Lishan
AU - Chen, Mingming
AU - Lee, Seong Whan
AU - Fei, Xuan
AU - Shen, Dinggang
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.
AB - Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.
KW - Brain functional network
KW - Graph Laplacian regularization
KW - Mild cognitive impairment (MCI)
KW - Sparse representation
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U2 - 10.1016/j.patcog.2019.01.015
DO - 10.1016/j.patcog.2019.01.015
M3 - Article
VL - 90
SP - 220
EP - 231
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
ER -