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

Autor: Wang S; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan. Electronic address: wangsr@mail.hfut.edu.cn., Tang H; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China; Industrial Automation Engineering Technology Research Center of Anhui Province, Hefei, 230009, China., Himeno R; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan., Solé-Casals J; Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, 08500, Spain; Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB, United Kingdom., Caiafa CF; Instituto Argentino de Radioastronomía-CONICET CCT La Plata/CIC-PBA/UNLP, V. Elisa, 1894, Argentina., Han S; Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, 08500, Spain; Image Processing Research Group, RIKEN Center for Advanced Photonics, RIKEN, Wako-Shi, Saitama, 351-0198, Japan., Aoki S; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan., Sun Z; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan. Electronic address: z.sun.kc@juntendo.ac.jp.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2024 Dec; Vol. 257, pp. 108419. Date of Electronic Publication: 2024 Sep 11.
DOI: 10.1016/j.cmpb.2024.108419
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare that they have no known competing Financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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