Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach

2024-08-29

arXiv.org

AI Summary

Early diagnosis of Alzheimer’s disease (AD) remains challenging due to symptom subtlety, modality heterogeneity, and differential diagnostic ambiguity. To address these issues, we propose an end-to-end multimodal classification framework integrating clinical assessments, cognitive scales, structural MRI, EEG, and longitudinal tabular data. Methodologically: (1) we introduce a novel cross-modal attention aggregation mechanism for dynamic alignment of heterogeneous features; (2) we design the TimesBlock module to capture long-range temporal dynamics in EEG signals; (3) we construct ADMC—the first private, tri-modal dataset comprising synchronized EEG, structural MRI, and structured tabular records. Evaluated on ADMC, our framework achieves state-of-the-art performance, significantly improving early discrimination accuracy among AD, mild cognitive impairment, and cognitively normal controls. Both code and the ADMC dataset are publicly released to foster reproducible and robust multimodal research in AD early diagnosis.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle symptoms and varied presentations, often leading to misdiagnosis with traditional unimodal diagnostic methods due to their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data to enhance diagnostic accuracy. The model incorporates a feature tagger with a tabular data coding architecture and utilizes the TimesBlock module to capture intricate temporal patterns in Electroencephalograms (EEG) data. By employing Cross-modal Attention Aggregation module, the model effectively fuses Magnetic Resonance Imaging (MRI) spatial information with EEG temporal data, significantly improving the distinction between AD, Mild Cognitive Impairment, and Normal Cognition. Simultaneously, we have constructed the first AD classification dataset that includes three modalities: EEG, MRI, and tabular data. Our innovative approach aims to facilitate early diagnosis and intervention, potentially slowing the progression of AD. The source code and our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet.

Problem

• Alzheimer's disease
• Early diagnosis
• Multi-information integration

Innovation

• Alzheimer's Diagnosis
• Multimodal Learning Model
• Comprehensive Dataset