In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 5

Dynamic population hypergraph convolutional network for autism spectrum disorder diagnosis

Wang Guohua1a
Wang Lifang2
Xue Xiaohong2
Wang Qianshan1b
Li Haifang1a,2
1. a. College of Computer Science & Technology (College of Data Science), b. College of Software, Taiyuan University of Technology Shanxi 030000, China
2. College of Computer & Information Engineering, Shanxi Technology & Business College Shanxi 030000, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0427

Abstract

In recent years, graph neural networks have been widely used in the diagnosis of autism. Most of the existing studies used manual calculation to construct inter-subject similarity maps to achieve diagnosis, and it is difficult to accurately model complex inter-subject relationships. In addition, most of the methods also ignored the use of the characteristics of the brain region itself. To solve the above problems, this work proposed a dynamic population hypergraph convolution autism diagnostic method that combines functional connectivity and dynamic amplitude of low frequency fluctuation features, constructed hypergraph topology from multiple perspectives, updated node embedding using the proposed dynamic hypergraph convolution. Then evaluated the proposed method on the publicly available dataset ABIDE-I containing 17 sites and achieve a classification accuracy of 87.4% between autistic and normal control, outperforming many existing methods. In addition, experiments were conducted on the ADHD-200 dataset and gradient-based significance maps were used to identify brain functional connections important for classification. The results show that the method has good generalization and the ability to mine potential biomarkers.

Foundation Support

山西省中央引导地方科技发展资金项目(YDZJSX2021C005)
山西省重点研发计划项目(2022ZDYF128)
山西省高等学校科技创新项目(2023L497)
山西省基础研究计划青年科学研究项目(20210302124272)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0427
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-03-06] Accepted Paper

Cite This Article

王国华, 王丽芳, 薛小红, 等. 一种基于动态人群超图卷积网络的自闭症诊断方法 [J]. 计算机应用研究, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0427. (Wang Guohua, Wang Lifang, Xue Xiaohong, et al. Dynamic population hypergraph convolutional network for autism spectrum disorder diagnosis [J]. Application Research of Computers, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0427. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)