Summary of Discovering Robust Biomarkers Of Psychiatric Disorders From Resting-state Functional Mri Via Graph Neural Networks: a Systematic Review, by Yi Hao Chan et al.
Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
by Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The review explores the application of graph neural networks (GNN) and model explainability techniques to functional magnetic resonance imaging (fMRI) datasets for disorder prediction tasks, with a focus on evaluating the robustness of potential biomarkers produced for psychiatric disorders. The study identifies 65 GNN-based studies that reported fMRI biomarkers for attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and schizophrenia published before October 2024. While most studies had performant models, the salient features highlighted varied greatly across studies on the same disorder, highlighting the need for objective evaluation metrics to determine robustness. The review suggests establishing new standards based on these metrics and presents a prediction-attribution-evaluation framework to set foundations for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers use brain scans (fMRI) to predict if someone has a mental health condition like ADHD or depression. It shows that different computer models can pick out different things from the brain scans that might be helpful in making these predictions. The review also finds that many of the things that these computer models highlight as important aren’t actually very useful for predicting the conditions. The paper suggests that we need to find a better way to evaluate how good these computer models are, and it proposes some ideas for how to do this. |
Keywords
» Artificial intelligence » Attention » Gnn
