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%.
Bibliographical noteFunding 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.
This work was supported by the National Natural Science Foundation of China (61773244, 82001775, 61772319, 61873177, 61972235, 61976125), the Yantai Key Research and Development Program of China (2017ZH065, 2019XDHZ081), the Shandong Provincial Key Research and Development Program of China (2019GGX101069) and the Doctoral Scientific Research Foundation of Shandong Technology and Business (BS202016). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright 2021 Xie et al.
- Autism spectrum disorder
- Central moment feature
- Cross validation
- Dynamic functional connectivity network
- Feature extraction
- Feature selection
- Functional connectivity
- Functional magnetic resonance imaging
- High functional connectivity network
- Low functional connectivity network
ASJC Scopus subject areas
- General Neuroscience
- General Biochemistry,Genetics and Molecular Biology
- General Agricultural and Biological Sciences