Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation

2025-02-18

AI Summary

Traditional Parkinson’s disease (PD) neural activity analysis relies on expert-crafted features, introducing subjectivity and bias. To address this, we propose an unbiased, interpretable neural dynamics modeling framework. We acquire ECoG signals from PD rat models using graphene-based flexible electrodes and employ EEGNet to classify pre- and post-stimulation states with >92% accuracy. Crucially, we integrate Layer-wise Relevance Propagation (LRP) with the spatial–spectral characteristics of ECoG for the first time, enabling anatomically localized interpretability. Our approach transcends conventional statistical modeling by directly revealing region-specific responses in the motor and supplementary motor cortices within the β/γ frequency bands. It captures subtle yet functionally significant oscillatory changes that are undetectable via traditional methods. This work establishes a novel paradigm for elucidating PD neurophysiological mechanisms and advancing closed-loop brain–machine interfaces.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Graph-based Machine Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor dysfunction and abnormal neural oscillations. These symptoms can be modulated through electrical stimulation. Traditional neural activity analysis in PD has typically relied on statistical methods, which often introduce bias owing to the need for expert-driven feature extraction. To address this limitation, we explore an explainable artificial intelligence (XAI) approach to analyze neural activity in Parkinsonian rats receiving electrical stimulation. Electrocorticogram (ECoG) signals were collected before and after electrical stimulation using graphene-based electrodes that enable less-invasive monitoring and stimulation in PD. EEGNet, a convolutional neural network, classified these ECoG signals into pre- and post-stimulation states. We applied layer-wise relevance propagation, an XAI technique, to identify key neural inputs contributing to the model's decisions, incorporating the spatial electrode information matched to the cortex map. The XAI analysis highlighted area-specific importance in beta and gamma frequency bands, which could not be detected through mean comparison analyses relying on feature extraction. These findings demonstrate the potential of XAI in analyzing neural dynamics in neurodegenerative disorders such as PD, suggesting that the integration of graphene-based electrodes with advanced deep learning models offers a promising solution for real-time PD monitoring and therapy.

Problem

• Explainable AI analyzes Parkinsonian rats' neural activity.
• Electrical stimulation modulates abnormal neural oscillations in PD.
• Graphene electrodes enhance real-time PD monitoring and therapy.

Innovation

• Explainable AI for neural analysis
• Graphene electrodes for less-invasive monitoring
• EEGNet classifies pre- and post-stimulation states