Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) early stages, among which graph-based more popular, because their capability to utilize relational information different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both static and dynamic connectivity features extracted from resting-state fMRI (rs-fMRI) data detect stages. First, our earlier state-space (SSM) dynamics characterization method is used modified model. After verifying its utility, this with designed intermediate level integration approach learning, construct To verify effectiveness models, models are applied on rs-fMRI data, ADNI dataset. Superior performance corroborates utility application.