PET-derived amyloid patterns in gray and white matter across the Alzheimer’s disease: A high-model-order ICA
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Background
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by gray matter (GM) changes, such as amyloid-beta (Aβ) plaques and neurofibrillary tangles. While GM alterations are well-established, the fine-grained, spatially distinct patterns of homogeneous Aβ uptake and their changes remain poorly understood. Additionally, white matter (WM) pathology is less explored. This study addresses these gaps by leveraging high-model-order independent component analysis (ICA) to identify spatially granular amyloid networks in both GM and WM.
Methods
We analyzed [18F]Florbetapir (FBP) PET images from 716 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), classified as cognitively normal (CN), mild cognitive impairment (MCI), or AD dementia. High-model-order ICA was applied to identify 80 GM and 13 WM FBP-related networks, which were labeled using terms from the Neuromark 2.2 Atlas. Statistical analyses assessed diagnostic effects and relationships between these networks and cognitive and neuropsychiatric variables.
Results
Significant diagnostic differences were observed across GM and WM networks, revealing a continuous pattern of change from CN to MCI to AD. The analysis showed that the extended hippocampal, extended thalamic, basal ganglia, and frontal subdomains displayed an MCI profile closer to AD than to CN. In contrast, other subdomains exhibited a more mixed pattern, with MCI sometimes aligning more closely with CN and other times with AD. Notably, the hippocampal–entorhinal complex (HEC) within the extended hippocampal subdomain and the precuneus within the default mode subdomain were consistently associated with cognitive decline, highlighting their roles in disease progression. Additionally, WM networks, particularly the retrolenticular internal capsule (RICap), also demonstrated significant relationships with cognitive measures, suggesting that AD pathology extends beyond GM and disrupts broader network connectivity. These findings were validated in an independent replication dataset.
Conclusion
High-model-order ICA effectively captures distinct fine-grained amyloid distributions, offering a network-level perspective that enhances our understanding of AD neurobiology. By decomposing complex PET signal patterns into distinct networks, this approach underscores the critical role of both GM and WM integrity in AD pathology. The consistent associations of the HEC, precuneus, and WM networks with cognitive decline highlight the widespread impact of AD-related pathology, emphasizing the value of this methodology in advancing AD research.
