Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

PeerJ

article

Qingsong Xie¹  Xiangfei Zhang¹  Islem Rekik²  Xiaobo Chen¹  Ning Mao⁴  Dinggang Shen⁵  Feng Zhao¹ 

[1] School of Computer Science and Technology, Shandong Technology and Business University;School of Science and Engineering, Computing, University of Dundee;BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University;Department of Radiology, Yantai Yuhuangding Hospital;School of Biomedical Engineering, ShanghaiTech University;Shanghai United Imaging Intelligence Co., Ltd.;Department of Artificial Intelligence, Korea University

关键词: Autism spectrum disorder;    Functional magnetic resonance imaging;    Functional connectivity;    High functional connectivity network;    Low functional connectivity network;    Dynamic functional connectivity network;    Central moment feature;    Feature extraction;    Feature selection;    Cross validation;   

DOI  :  10.7717/peerj.11692

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.

【 授权许可】

CC BY