TY - JOUR
T1 - Remodeling pearson’s correlation for functional brain network estimation and autism spectrum disorder identification
AU - Li, Weikai
AU - Wang, Zhengxia
AU - Zhang, Limei
AU - Qiao, Lishan
AU - Shen, Dinggang
N1 - Funding Information:
This work was partly supported by National Natural Science Foundation of China (61300154, 61402215, 11401060), Natural Science Foundation of Shandong Province (2014ZRB019E0, 2014ZRB019VC), Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1500501, KJ1600512, Kj1600518), Chongqing Graduate Student Research Innovation Project (CYS16183), and NIH grants (AG041721, MH107815, EB006733, EB008374, EB009634).
PY - 2017/8/31
Y1 - 2017/8/31
N2 - Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson’s Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegantmathematical formulation for sparsifying PC-based networks.More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autismspectrumdisorders (ASD) fromnormal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52%classification accuracy which outperforms the baseline and state-of-the-art methods.
AB - Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson’s Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegantmathematical formulation for sparsifying PC-based networks.More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autismspectrumdisorders (ASD) fromnormal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52%classification accuracy which outperforms the baseline and state-of-the-art methods.
KW - Autism spectrum disorder
KW - Functional brain network
KW - Functional magnetic resonance imaging
KW - Pearson’s correlation
KW - Scale-free
KW - Sparse representation
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U2 - 10.3389/fninf.2017.00055
DO - 10.3389/fninf.2017.00055
M3 - Article
AN - SCOPUS:85029220161
VL - 11
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
SN - 1662-5196
M1 - 55
ER -