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

TY - JOUR

T1 - Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

AU - Xie, Qingsong

AU - Zhang, Xiangfei

AU - Rekik, Islem

AU - Chen, Xiaobo

AU - Mao, Ning

AU - Shen, Dinggang

AU - Zhao, Feng

N1 - Funding Information: The following grant information was disclosed by the authors: National Natural Science Foundation of China: 61773244, 82001775, 61772319, 61873177, 61972235, 61976125. Yantai Key Research and Development Program of China: 2017ZH065 and 2019XDHZ081. Shandong Provincial Key Research and Development Program of China: 2019GGX101069. Doctoral Scientific Research Foundation of Shandong Technology and Business: BS202016. Publisher Copyright: Copyright 2021 Xie et al.

PY - 2021/7/6

Y1 - 2021/7/6

N2 - 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%.

AB - 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%.

KW - Autism spectrum disorder

KW - Central moment feature

KW - Cross validation

KW - Dynamic functional connectivity network

KW - Feature extraction

KW - Feature selection

KW - Functional connectivity

KW - Functional magnetic resonance imaging

KW - High functional connectivity network

KW - Low functional connectivity network

UR - https://www.scopus.com/pages/publications/85109303016

U2 - 10.7717/peerj.11692

DO - 10.7717/peerj.11692

M3 - Article

C2 - 34268010

AN - SCOPUS:85109303016

SN - 2167-8359

VL - 7

JO - PeerJ

JF - PeerJ

M1 - e11692

ER -