Identifying Robust Biomarkers of Neurological Disorders from Functional MRI Using Graph Neural Networks

Core Concepts

Graph neural networks (GNNs) have emerged as a powerful tool for modeling functional magnetic resonance imaging (fMRI) data to identify potential biomarkers of neurological disorders. Recent studies have reported significant improvements in disorder classification performance and highlighted salient features that could serve as potential biomarkers. However, the robustness of these potential biomarkers remains a key challenge.

Abstract

This review provides an overview of how GNNs and model explainability techniques have been applied on fMRI datasets for neurological disorder prediction and biomarker discovery. The focus is on evaluating the robustness of potential biomarkers identified for neurodegenerative diseases (dementia, Parkinson's disease) and neuropsychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and schizophrenia).
The key insights are:

While most studies have performant GNN models, the salient features highlighted as potential biomarkers vary greatly across studies on the same disorder.
Little has been done to systematically evaluate the robustness of these potential biomarkers.
To address these issues, the review suggests establishing new standards based on objective evaluation metrics to determine the robustness of potential biomarkers discovered via GNNs.
The review also highlights gaps in the existing literature and proposes a prediction-attribution-evaluation framework that could set the foundations for future research on improving the robustness of potential biomarkers discovered using GNNs.

Stats

"Graph neural networks 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."

Quotes

"To ensure that salient features are truly representative of disorder traits (and not mere artefacts), it would be prudent to establish new standards of reporting salient features."
"Existing reviews on biomarkers tend to summarise various types of biomarkers (often going beyond fMRI) and do not focus on evaluating the reliability of the computational techniques used to derive the biomarkers."

Key Insights Distilled From

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review

by Yi Hao Chan,... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00577.pdf

Deeper Inquiries

How can the robustness of potential biomarkers discovered using GNNs be further improved beyond the evaluation metrics proposed in this review

To further improve the robustness of potential biomarkers discovered using GNNs, several strategies can be implemented beyond the evaluation metrics proposed in the review.

Cross-validation and External Validation: Implementing robust cross-validation techniques such as nested cross-validation can help assess the generalizability of the biomarkers across different datasets. Additionally, external validation on independent datasets can validate the reproducibility of the biomarkers.

Ensemble Methods: Utilizing ensemble methods by combining multiple GNN models can help reduce overfitting and enhance the stability of the identified biomarkers. By aggregating predictions from diverse models, the ensemble can provide more reliable and robust biomarkers.

Feature Selection Techniques: Incorporating feature selection methods such as recursive feature elimination or L1 regularization can help identify the most informative features and reduce the impact of noise in the data, leading to more robust biomarkers.

Data Augmentation: Augmenting the fMRI datasets with techniques like rotation, flipping, or adding noise can help increase the variability in the data and improve the robustness of the biomarkers by making them less sensitive to small variations in the input.

Interpretability and Explainability: Enhancing the interpretability of the GNN models by incorporating explainable AI techniques can provide insights into how the biomarkers are derived, increasing trust and understanding of the results.

Collaborative Research and Reproducibility: Encouraging collaboration among researchers to replicate and validate findings can enhance the robustness of biomarkers. Sharing data, code, and methodologies openly can facilitate reproducibility and ensure the reliability of the discovered biomarkers.

What are the potential limitations of using GNNs for biomarker discovery, and how can these limitations be addressed

Using GNNs for biomarker discovery may have certain limitations that need to be addressed to improve the effectiveness of the approach:

Interpretability: GNNs are often considered black-box models, making it challenging to interpret how they arrive at their predictions. Addressing this limitation by developing more interpretable GNN architectures or incorporating explainability techniques can enhance the trustworthiness of the biomarkers.

Data Quality and Quantity: The quality and quantity of fMRI data can significantly impact the performance of GNNs. Ensuring high-quality data collection, preprocessing, and augmentation techniques can mitigate the limitations posed by limited or noisy datasets.

Model Complexity: Complex GNN architectures may lead to overfitting and difficulty in generalizing to new datasets. Simplifying the model architecture, regularization techniques, and hyperparameter tuning can help mitigate this limitation.

Biological Interpretation: Linking the identified biomarkers to underlying biological mechanisms is crucial for clinical relevance. Collaborating with domain experts and integrating prior knowledge of neurobiology can enhance the biological interpretability of the discovered biomarkers.

Heterogeneity and Generalizability: Neurological disorders exhibit heterogeneity, making it challenging to generalize biomarkers across diverse populations. Addressing this limitation requires robust validation on diverse datasets and populations to ensure the generalizability of the biomarkers.

How can the insights from this review on improving the robustness of biomarkers be extended to other neuroimaging modalities beyond fMRI

The insights from this review on improving the robustness of biomarkers discovered using GNNs can be extended to other neuroimaging modalities beyond fMRI by considering the following strategies:

Adaptation to Different Data Structures: Tailoring GNN architectures to suit the specific data structures of other neuroimaging modalities such as EEG, MEG, or PET scans. Customizing the model design to accommodate the unique characteristics of each modality can enhance the robustness of biomarker discovery.

Feature Engineering and Selection: Implementing feature engineering techniques specific to the data characteristics of each modality can help identify relevant biomarkers. Utilizing feature selection methods to extract the most informative features can improve the interpretability and reliability of the biomarkers.

Model Explainability: Incorporating explainability techniques that are suitable for the data representation of different neuroimaging modalities can provide insights into the discovered biomarkers. Ensuring that the explanations are coherent and reliable across modalities can enhance the trustworthiness of the biomarkers.

Validation and Reproducibility: Conducting rigorous validation and reproducibility studies on diverse datasets from various neuroimaging modalities can validate the generalizability of the biomarkers. Collaborative research efforts and open sharing of data and methodologies can facilitate the extension of insights to different modalities.