A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals

TL;DR

This paper introduces a deep spatio-temporal attention model using transformer-convolution architecture to detect early Alzheimer's disease from fMRI data, outperforming existing methods and highlighting key brain networks involved in disease progression.

Contribution

The study presents a novel deep learning framework with spatial-temporal self-attention for early AD detection using dFNC, improving prediction accuracy and interpretability.

Findings

Higher similarity between MCI and asymptomatic AD in dFNC patterns

Model emphasizes intra-visual domain connectivity for asymptomatic AD detection

Deep learning approach outperforms traditional machine learning methods

Abstract

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatment. Functional magnetic resonance imaging (fMRI), particularly dynamic functional network connectivity (dFNC), has emerged as an important biomarker for AD. Nevertheless, studies probing at-risk subjects in the pre-symptomatic stage using dFNC are limited. To identify at-risk subjects and understand alterations of dFNC in different stages, we leverage deep learning advancements and introduce a transformer-convolution framework for predicting at-risk subjects based on dFNC, incorporating spatial-temporal self-attention to capture brain network dependencies and temporal dynamics. Our model significantly outperforms other popular machine learning methods. By analyzing individuals with diagnosed AD and mild cognitive impairment (MCI), we studied the AD progression and observed a higher similarity between MCI and asymptomatic AD. The interpretable analysis highlights the cognitive-control network's diagnostic importance, with the model focusing on intra-visual domain dFNC when predicting asymptomatic AD subjects.