End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligence (2407.12553v1)

Abstract: In this paper, we propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data derived from magnetic resonance imaging. Ultimately, this representation is used within a directed graph convolutional architecture and investigated with explainable AI tools. Stroke is one of the leading causes of mortality and morbidity worldwide, and it demands precise diagnostic tools for timely intervention and improved patient outcomes. Neuroimaging data, with their rich structural and functional information, provide a fertile ground for biomarker discovery. However, the complexity and variability of information flow in the brain requires advanced analysis, especially if we consider the case of disrupted networks as those given by the brain connectome of stroke patients. To address the needs given by this complex scenario we proposed an end-to-end pipeline. This pipeline begins with reservoir computing causality, to define effective connectivity of the brain. This allows directed graph network representations which have not been fully investigated so far by graph convolutional network classifiers. Indeed, the pipeline subsequently incorporates a classification module to categorize the effective connectivity (directed graphs) of brain networks of patients versus matched healthy control. The classification led to an area under the curve of 0.69 with the given heterogeneous dataset. Thanks to explainable tools, an interpretation of disrupted networks across the brain networks was possible. This elucidates the effective connectivity biomarker's contribution to stroke classification, fostering insights into disease mechanisms and treatment responses.

• The paper presents a novel end-to-end pipeline integrating reservoir computing with explainable AI to analyze effective connectivity in stroke imaging.
• It employs graph convolutional neural networks to classify connectivity maps, achieving an AUC of 0.69 for distinguishing stroke from control subjects.
• LIME-based interpretability highlights key brain regions, offering actionable insights for clinical decision support and future neuroimaging applications.

End-to-End Stroke Imaging Analysis Using Reservoir Computing-Based Effective Connectivity and Interpretable AI

Overview

The paper by Ciezobka et al. focuses on a novel end-to-end pipeline for analyzing stroke imaging data using a combination of reservoir computing-based effective connectivity and interpretable AI. It aims to define an efficient brain representation for connectivity in stroke data derived from MRI, which is subsequently classified and interpreted using directed graph convolutional architectures enhanced with explainable AI tools. This paper addresses a critical challenge in understanding the dynamic disruptions within brain networks caused by stroke and offers a methodological advancement for diagnosing and managing stroke patients.

Methods

Data and Preprocessing

MRI and behavioral data from stroke patients and healthy controls were used, acquired with a Siemens 3T Tim-Trio scanner consisting of T1-weighted, T2-weighted, and diffusion tensor images, along with resting-state functional MRI (fMRI). After preprocessing, including quality control and parcellation into 100 regions of interest (ROIs), the time series for each participant were fed into the proposed pipeline to generate subject-specific effective connectivity maps.

Reservoir Computing

Reservoir Computing Networks (RCNs) were employed to define causal interactions within the brain. These networks leverage the dynamics of recurrent neural networks to process temporal data, offering a balance of computational efficiency and predictive accuracy. The reservoir state, driven by the input time series, captures the dynamics of brain regions, which is fundamental for understanding the directionality and causality of neural interactions.

The core of the analysis is based on the computation of predictability scores $\rho_{x \rightarrow y}(\tau)$, representing the Pearson correlation between true time series and predicted series at different lags. Directionality is determined through the scoring system, $\Delta_{x \rightarrow y}(\tau)$, which gauges potential causality by comparing the predictability scores between nodes. The resultant effective connectivity matrix $\mathbf{A}_\tau$ captures these directed relationships across the brain's regions.

Graph Convolutional Neural Networks

Graph Convolutional Neural Networks (GCNs) were utilized to classify the effective connectivity maps derived from reservoir computing. GCNs are well-suited for this task due to their ability to handle the complex topological structures inherent in brain network data. An additional step involved creating feature vectors for nodes using the Local Degree Profile (LDP), which provided an efficient means of leveraging node properties within the directed graphs for classification.

Explainability with LIME

Local Interpretable Model-agnostic Explanations (LIME) were integrated to elucidate the contribution of specific brain regions and connections to the classification task. This interpretability step is crucial for clinical applications, enabling the identification of which brain areas most influence the classification between stroke and control subjects.

Results

The paper demonstrated several key findings:

1. Effective Connectivity Maps: Group-averaged effective connectivity maps showed distinguishable patterns of hemispheric segregation and inter-hemispheric connectivity, significant for subjects with right-hemispheric strokes. This asymmetry aligns with previous findings of disrupted inter-hemispheric communication in stroke patients.
2. Classification Performance: The pipeline achieved an area under the curve (AUC) of 0.69 in classifying stroke versus control subjects using the GCN. This performance is notable given the dataset’s heterogeneity, highlighting the pipeline’s robustness compared to traditional Granger Causality methods.
3. Node and Edge Importance: LIME analysis revealed that regions within the visual, dorsal, and ventral attention networks significantly contributed to identifying stroke patients. This insight aligns with known functional disruptions in attention-related networks post-stroke.

Implications

The practical implications of this research are multifaceted:

• Clinical Decision Support: By improving the interpretability and accuracy of stroke diagnosis through effective connectivity maps and explainable AI, clinicians can make more informed decisions about interventions and treatment strategies.
• General Applicability: Although focused on stroke, the proposed methods can be generalized to other brain disorders, enhancing the understanding of network functionality and disruptions in various pathologies.
• Future Developments: Further research could refine these methods, integrate larger datasets, and explore more sophisticated graph learning techniques to enhance robustness and accuracy. Additionally, the causal interactions captured by reservoir computing could become a standard tool for functional brain network analysis.

Conclusion

This paper presents a comprehensive approach to stroke imaging analysis, combining the strengths of reservoir computing and advanced graph neural networks. By providing detailed insights into brain network disruptions and enhancing the interpretability of classification outcomes, it sets the stage for advancing both theoretical understanding and practical applications in neuroimaging and clinical neurology. The work marks a significant step toward integrating explainable AI into neuroimaging pipelines, fostering greater confidence and utility in clinical settings.