Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer

2025-01-27

AI Summary

To address the challenge of accurately identifying mild cognitive impairment (MCI) and early-stage Alzheimer’s disease (AD), this paper proposes a novel temporal modeling framework for dynamic functional connectivity (dFC). We introduce spatiotemporal joint modeling—first applied to dFC analysis—via a dual-path Transformer architecture incorporating spatiotemporal parallel blocks, enabling simultaneous capture of both spatial topological organization among brain regions and their temporal dynamics. Furthermore, we propose a brain-state-oriented contrastive self-supervised learning strategy to overcome limitations of conventional methods relying on static functional connectivity or isolated temporal modeling. Evaluated on the ADNI dataset comprising 345 subjects and 570 resting-state fMRI scans, our method achieves significantly improved MCI classification accuracy. Results demonstrate its effectiveness, robustness, and clinical potential for detecting prodromal AD.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning

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
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities, and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding window strategy. Then, we simultaneously employ a temporal block and a spatial block to capture higher-order representations of dynamic spatio-temporal dependencies, via mapping them into an efficient fused feature representation. To further enhance the robustness of these feature representations by reducing the dependency on labeled data, we also introduce a contrastive learning strategy to manipulate different brain states. Experimental results on 345 subjects with 570 scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the superiority of our proposed method for MCI (Mild Cognitive Impairment, the prodromal stage of AD) prediction, highlighting its potential for early identification of AD.

Problem

• Dynamic Functional Connectivity
• Alzheimer's Disease
• Mild Cognitive Impairment

Innovation

• Spatial Temporal Transformer
• Contrastive Learning
• Dynamic Functional Connectivity (dFC)