Schedule of Events | Symposia

Greater amyloid accumulation in cognitive networks in Preclinical Alzheimer’s Disease

Poster Session D - Monday, March 31, 2025, 8:00 – 10:00 am EDT, Back Bay Ballroom/Republic Ballroom

Sara Nolin¹, Stephanie Aghamoosa¹, Averi Jones¹, Andreana Benitez¹; ¹MUSC

Introduction: Preclinical Alzheimer’s disease (pAD) is defined by elevated brain amyloid-beta prior to the onset of symptoms. We investigated whether amyloid accumulation in pAD differs across cognitive and non-cognitive functional brain networks and relates to cognitive performance. Methods: 157 cognitively unimpaired participants aged 45–84 underwent neuropsychological testing, brain MRI, and amyloid PET (18F-florbetapir) at baseline and 2-year follow-up (M=2.06, SD=.25 years; n=138). At baseline, 28 were amyloid positive (AB+) based on neuroradiological read. We calculated amyloid burden as the mean standard uptake value ratio (mSUVr) within cognitive networks (default mode, fronto-parietal, ventral attention, dorsal attention, limbic) and non-cognitive networks (visual, somato-motor). Linear mixed models tested the 1) main effects and interaction of network type (cognitive, non-cognitive) and age and 2) main effects and interaction of group (AB+, AB-) and network type, covarying age. We correlated mSUVr within cognitive networks with baseline and change in composite cognitive scores. Results: mSUVr was greater in cognitive than non-cognitive networks, and this difference increased with age. There was a group-by-network interaction, whereby the effect of higher mSUVr in cognitive vs. non-cognitive networks was greater in the AB+ than AB- group. Cognitive network mSUVr was not correlated with baseline or change in cognition. Conclusion: Amyloid preferentially accumulates in cognitive functional networks in pAD, and this discrepancy increases with age. Amyloid burden in cognitive networks was not related to cognitive performance in this unimpaired sample. These findings highlight the importance of targeting cognitive networks in early detection and intervention.

Topic Area: METHODS: Neuroimaging