Back

PET-derived amyloid patterns in gray and white matter across Alzheimer's disease: A high-model-order ICA

Khasayeva, N.; Jensen, K. M.; Eierud, C.; Petropoulos, H.; Luo, R.; Premi, E.; Borroni, B.; Chen, J.; Calhoun, V.; Iraji, A.

2025-03-24 neuroscience
10.1101/2025.03.21.644614 bioRxiv
Show abstract

INTRODUCTIONAlzheimers Disease (AD) is a neurodegenerative disorder marked by gray matter (GM) changes driven by amyloid-beta (A{beta}) plaques and neurofibrillary tangles. While GM alterations are well documented, spatially distinct patterns of homogeneous A{beta} uptake and white matter (WM) involvement remain underexplored. METHODSWe 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. RESULTSIdentified 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. DISCUSSIONOur findings highlight the utility of high-order ICA in identifying reproducible A{beta} networks and the contribution of WM networks, such as the posterior corpus callosum, in the early pathological landscape of AD.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above