A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals
Journal:
arXiv
Published Date:
Aug 1, 2024
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.
