[2405.00577] Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2405.00577 (cs) [Submitted on 1 May 2024 ( v1 ), last revised 1 Feb 2025 (this version, v2)] Title: Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review Authors: Yi Hao Chan , Deepank Girish , Sukrit Gupta , Jing Xia , Chockalingam Kasi , Yinan He , Conghao Wang , Jagath C. Rajapakse View a PDF of the paper titled Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review, by Yi Hao Chan and 7 other authors View PDF HTML (experimental) Abstract: Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. However, existing methods of evaluating their robustness are often limited to cross-referencing with existing literature, which is a subjective and inconsistent process. In this review, we provide an overview of how GNN and model explainability techniques (specifically, feature attributors) have been applied to fMRI datasets for disorder prediction tasks, with an emphasis on evaluating the robustness of potential biomarkers produced for psychiatric disorders. Then, 65 studies using GNNs that reported potential fMRI biomarkers for psychiatric disorders (attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia) published before 9 October 2024 were identified from 2 online databases (Scopus, PubMed). We found that while most studies have performant models, salient features highlighted in these studies (as determined by feature attribution scores) vary greatly across studies on the same disorder. Reproducibility of biomarkers is only limited to a small subset at the level of regions and few transdiagnostic biomarkers were identified. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on discovering robust biomarkers of psychiatric disorders via GNNs. Subjects: Machine Learning (cs.LG) ; Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2405.00577 [cs.LG] (or arXiv:2405.00577v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2405.00577 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1016/j.neuroimage.2025.121422 Focus to learn more DOI(s) linking to related resources Submission history From: Yi Hao Chan [ view email ] [v1] Wed, 1 May 2024 15:29:55 UTC (2,014 KB) [v2] Sat, 1 Feb 2025 09:26:02 UTC (484 KB) Full-text links: Access Paper: View a PDF of the paper titled Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review, by Yi Hao Chan and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-05 Change to browse by: cs eess eess.SP q-bio q-bio.NC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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