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

TL;DR

This paper presents a multimodal learning model integrating clinical, cognitive, neuroimaging, and EEG data to improve early Alzheimer's Disease detection, achieving higher diagnostic accuracy and constructing a novel multi-modal dataset.

Contribution

It introduces an integrated multimodal classification framework with novel modules and constructs the first comprehensive AD dataset including EEG, MRI, and tabular data.

Findings

Enhanced diagnostic accuracy for AD, MCI, and normal cognition.

Effective fusion of MRI and EEG data improves classification.

Availability of a new multi-modal AD dataset.

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.