Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition
点击次数：
影响因子：
5.9
DOI码：
10.1016/j.neuroimage.2020.117493
发表刊物：
NeuroImage
项目来源：
国家自然科学青年基金（11901583）
摘要：
Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized
networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique
subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.
合写作者：
Feng Vankee Lin, Martin Cole, Zhengwu Zhang
第一作者：
Lu Wang
论文类型：
期刊论文
卷号：
225
是否译文：
否
收录刊物：
SCI
学位：博士学位
职称：副教授
学科：统计学
所在单位：数学与统计学院