Multi-scale graph theoretical analysis of resting-state fMRI for classification of Alzheimer’s disease, mild cognitive impairment, and healthy controls

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline, making early detection vital for timely intervention. However, early diagnosis is challenging due to the heterogeneous presentation of symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous brain activity and functional connectivity, which are known to be disrupted in AD and mild cognitive impairment (MCI). Traditional methods, such as Pearson’s correlation, have been used to calculate association matrices, but these approaches often overlook the dynamic and non-stationary nature of brain activity. In this study, we introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks. Our approach captures the time-frequency representation of brain activity, allowing for a more nuanced analysis of

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Recommended citation: Khazaee, Ali; Mohammadi, Abdolreza; O’Reilly, Ruairi (2025). “Multi-scale graph theoretical analysis of resting-state fMRI for classification of Alzheimer’s disease, mild cognitive impairment, and healthy controls.” Signal, Image and Video Processing (Springer Nature).