A Review on Structural and Functional Connectivity-Based Alzheimer’s Disease Detection and Localization in Functional Magnetic Resonance Imagi

Abstract

The most prevalent sort of dementia, which accounts for at least two-thirds of cases in individuals aged 65 as well as older, is Alzheimer's disease (AD). AD is a neurodegenerative illness with insidious onset along with advanced impairment of behavioural as well as cognitive functions. Memory, language, comprehension, attention, reasoning, along with judgment is encompassed in those functions. Functional Magnetic Resonance Imaging (fMRI), as a neuroimaging methodology, affords a means of examining the structure along with function of brain connectivity linked with AD. The recent advancements’ usage in Machine Learning (ML), Deep Learning (DL), graph theory, along with connection analysis has allowed for the automated identification and localization of aberrant neural activity from fMRI data. Here, the aim is to review existing structural and Functional Connectivity (FC) detection and localization methods of AD using fMRI techniques. As per the outcome, using a combination of structural along with FC analyses with AI-based approaches enhances the accuracy of diagnosing AD, the precision of localizing the disease, and the predictability of the disease at an early stage. Thus, the integration of FMRI with advanced techniques can enhance diagnostic accuracy, enable precise localization of AD, and improve early-stage AD prediction by supporting better clinical outcomes and patient care.