Corrigendum: Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset

• Published in: Frontiers in neuroscience Vol. 14; p. 243
• Main Authors: Li, Yao, Sun, Chao, Li, Pengzu, Zhao, Yunpeng, Mensah, Godfred Kim, Xu, Yong, Guo, Hao, Chen, Junjie
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 02.04.2020; Frontiers Media S.A
• Subjects: classification; cluster coefficients based on pairs of nodes; depression; hypernetwork; multi-feature; Neuroscience; sparse group lasso; cluster coefficients based on pairs of nodes; depression; classification; hypernetwork; multi-feature; sparse group lasso
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2020.00243

The Materials and Methods section, subsection Construction of Hypernetwork, sub-subsection Sparse Linear Regression Model, paragraph 2: “The average time series of m-th ROI for n-th subject, xnm=Anmαnm+τnm xmn=Amnαmn+τmn , can be viewed as a response vector, which can be estimated as a linear combination of time series of other ROIs. The sparse linear regression model is specifically expressed as follows: xnm=Anmαnm+τnm (1) xmn=Amnαmn+τmn (1) where xnm=[xnm(1);xnm(2);…;xnm(T)] xmn=[xmn(1);xmn(2);…;xmn(T)] refers to the average time series of the m-th ROI for n-th subjects, with T being the number of time points in the time series; Anm=[xn1,…,xnm−1,0,xnm+1…,xnM] Amn=[x1n,…,xm-1n,0,xm+1n…,xMn] denotes the data matrix of the m-th ROI (all the average time series except for the m-th brain region, and the average time series of the m-th ROI being set to 0); αnm=[αn1,…,αnm−1,0,αnm+1…,αnM] αmn=[α1n,…,αm-1n,0,αm+1n…,αMn] denotes the coefficient vector that quantifies the degree of influence from the other ROI to the m-th ROI; and τnm τmn denotes a noise term, being Gaussian. [...]a hypernetwork is a 90*810 matrix.