Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer
Journal:
arXiv
Published Date:
Jan 27, 2025
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.
