Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Shurun Wang , Hao Tang , Ryutaro Himeno , Jordi Solé-Casals , Cesar F. Caiafa , Shuning Han , Shigeki Aoki , Zhe Sun
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引用次数: 0

Abstract

Background and Objective:

The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions.

Methods:

This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model’s predictions are both accurate and comprehensible.

Results:

The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively.

Conclusion:

Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

利用进化算法优化用于精神分裂症谱系障碍预测的图神经网络架构
背景和目的:精神分裂症谱系障碍的准确诊断在改善患者预后、及时干预和优化治疗方案方面发挥着重要作用。利用功能性磁共振成像数据进行的功能连接分析已被证明能为临床诊断提供宝贵的生物标志物。方法:本文提出了一种基于进化算法(EA)的图神经架构搜索(GNAS)方法。EA-GNAS能够搜索出用于精神分裂症谱系障碍预测的高性能图神经网络。结果:结果表明,利用遗传算法搜索得到的图神经网络模型在五倍交叉验证下表现优异,达到了0.1850的适配度。结论:基于精神分裂症谱系障碍患者的多站点数据集,研究结果表明该方法优于之前的方法,促进了我们对大脑功能的理解,并有可能成为诊断精神分裂症谱系障碍的生物标志物。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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