Mapping Amyloid Networks in Alzheimer’s Disease: A High-Order ICA Approach to Gray and White Matter Pathology
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INTRODUCTION
Alzheimer’s Disease (AD) is a neurodegenerative disorder marked by gray matter (GM) changes driven by amyloid-beta (Aβ) plaques and neurofibrillary tangles. While GM alterations are well documented, spatially distinct patterns of homogeneous Aβ uptake and white matter (WM) involvement remain underexplored.
METHODS
We applied high-order independent component analysis (ICA) to 716 [18F]Florbetapir PET scans, identifying 80 GM and 13 WM networks. Diagnostic and cognitive associations were evaluated via statistical modeling.
RESULTS
Identified networks delineated a progression trajectory, with mild cognitive impairment (MCI) profiles in temporoparietal and frontal subdomains more closely aligned with AD than cognitively normal (CN) profiles. GM networks, including the hippocampal-entorhinal complex and precuneus, and WM networks, including the retrolenticular internal capsule, demonstrated robust associations with cognitive performance.
DISCUSSION
Our findings highlight the utility of high-order ICA in identifying reproducible Aβ networks and the contribution of WM networks, such as the posterior corpus callosum, in the early pathological landscape of AD.
