ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification

2026-01-17

AI Summary

This study addresses the challenge of early and accurate differentiation between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) by proposing a unified graph learning framework. The approach uniquely integrates autoregressive moving average (ARMA) graph filtering with a reconstruction-driven objective to effectively model both local and global connectivity patterns in brain networks while mitigating the oversmoothing issue commonly observed in graph neural networks. Leveraging 20-bin fractional anisotropy (FA) histogram features derived from white matter regions, the method is trained and validated on multi-site diffusion MRI (dMRI) data. Experimental results on the multi-center ADNI and NIFD datasets demonstrate that the proposed framework significantly outperforms current state-of-the-art methods in classification performance.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is essential for reducing the risk of progression to severe disease stages. As AD and FTD propagate along white-matter regions in a global, graph-dependent manner, graph-based neural networks are well suited to capture these patterns. Hence, we introduce ARMARecon, a unified graph learning framework that integrates Autoregressive Moving Average (ARMA) graph filtering with a reconstruction-driven objective to enhance feature representation and improve classification accuracy. ARMARecon effectively models both local and global connectivity by leveraging 20-bin Fractional Anisotropy (FA) histogram features extracted from white-matter regions, while mitigating over-smoothing. Overall, ARMARecon achieves superior performance compared to state-of-the-art methods on the multi-site dMRI datasets ADNI and NIFD.

Problem

• neurodegenerative dementias
• Alzheimer's Disease
• Frontotemporal Dementia
• graph neural network
• early detection

Innovation

• ARMA graph filter
• graph neural network
• reconstruction-driven learning
• fractional anisotropy histogram
• neurodegenerative dementia classification