Journal of neurology · 2026
Integrating functional network topology, synaptic density, and tau pathology to predict cognitive decline in amnestic mild cognitive impairment.
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Lien Chih-Hao, Vanderlinden Greet, Vande Casteele Thomas, Laroy Maarten, Van Cauwenberge Margot G A, Vansteelandt Kristof, Koole Michel, Peeters Ronald, Sunaert Stefan, Bouckaert Filip, Emsell Louise, Van Laere Koen, Van den Stock Jan, Vandenbulcke Mathieu
Аннотация
BACKGROUND: Amnestic mild cognitive impairment (aMCI) represents a prodromal stage of Alzheimer's disease and carries a high risk of progression to dementia. Disruptions in functional brain networks, synaptic degeneration, and tau pathology each serve as neural markers of cognitive decline in aMCI. In this study, we employed a multimodal neuroimaging approach to determine whether integrating these markers provides a more powerful explanation of cognitive deterioration than examining them individually.
METHODS: Twenty-eight aMCI patients and 24 healthy controls underwent cognitive assessment, resting-state fMRI, 11C-UCB-J PET (synaptic density), and 18F-MK-6240 PET (tau). Eighteen aMCI patients were reassessed after 2 years. Graph-theoretical measures of network topology were derived, and linear regression models were used to examine whether combining functional, synaptic, and tau markers improved prediction of cognitive decline in aMCI.
RESULTS: Longitudinal changes in right hippocampal characteristic path length, synaptic density, and tau accumulation jointly predicted decline in delayed memory recall, achieving a higher predictive performance (adjusted R2 = 0.753) than unimodal models (adjusted R2 range: - 0.009-0.421), with reduced overfitting degree.
CONCLUSIONS: Multimodal neuroimaging integrating functional network topology, synaptic density, and tau burden improves prediction of memory decline in aMCI. These findings highlight complementary neural processes underlying progression and support multimodal imaging as a valuable approach for monitoring in prodromal Alzheimer's disease.
METHODS: Twenty-eight aMCI patients and 24 healthy controls underwent cognitive assessment, resting-state fMRI, 11C-UCB-J PET (synaptic density), and 18F-MK-6240 PET (tau). Eighteen aMCI patients were reassessed after 2 years. Graph-theoretical measures of network topology were derived, and linear regression models were used to examine whether combining functional, synaptic, and tau markers improved prediction of cognitive decline in aMCI.
RESULTS: Longitudinal changes in right hippocampal characteristic path length, synaptic density, and tau accumulation jointly predicted decline in delayed memory recall, achieving a higher predictive performance (adjusted R2 = 0.753) than unimodal models (adjusted R2 range: - 0.009-0.421), with reduced overfitting degree.
CONCLUSIONS: Multimodal neuroimaging integrating functional network topology, synaptic density, and tau burden improves prediction of memory decline in aMCI. These findings highlight complementary neural processes underlying progression and support multimodal imaging as a valuable approach for monitoring in prodromal Alzheimer's disease.