Effect of SUVR reference region selection on sampled iterative local approximation estimates of amyloid onset age

Background Amyloid PET imaging quantifies Aß plaques informing longitudinal modeling methods such as sampled iterative local approximation (SILA) that provide individualized estimated amyloid onset age (EAOA). EAOA is defined as the transition to abnormal Aß accumulation that occurs in Alzheimer’s disease. This work investigates the effect of SUVR reference region selection on EAOA by comparing DVR‐trained versus SUVR‐trained SILA models with three different SUVR reference regions. Method N = 585 participants (mean (SD) baseline age = 66.40 (7.89) years, 512 unimpaired, 44 MCI, 28 dementia, 1 unknown) from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center underwent T1‐weighted MRI and dynamic 11C‐Pittsburgh Compound B (PiB) amyloid PET imaging (0‐70 minutes after bolus injection). Reconstructed dynamic PET time series were processed and quantified using MR‐guided imaging pipelines optimized for DVR (Logan, k2’ = 0.149 min‐1, cerebellum gray matter (CBLM‐GM) reference region) and SUVR (50‐70 min frames) quantification with CBLM‐GM, whole cerebellum (WHOLE‐CBLM), or subcortical white matter (WM) as a reference region. Mean cortical PiB DVR and SUVR were each calculated by averaging across eight bilateral ROIs. Group‐based trajectory modeling was applied to longitudinal DVR data to establish an amyloid positivity (A+) threshold (DVR>1.14; equivalent to 14 centiloids). LME was used to define the relationship between DVR and SUVR and translate the DVR threshold to SUVR (SUVRCBLM GM>1.28; SUVRWHOLE CBLM>1.07; SUVRWM>0.68). Within‐person paired differences between EAOADVR and EAOASUVR_REFERENCE_REGION are reported as (mean; [95% CI]). Result EAOA derived from DVR and SUVR were compared for n = 198 (34%) DVR A+ participants. Amyloid vs. age data are shown for each quantification approach in Figure 1. EAOASUVR‐WHOLE‐CBLM (‐1.29 years; [‐1.51, ‐1.08]) had the highest agreement with EAOADVR compared to EAOASUVR‐CBLM‐GM (‐1.44 years; [‐1.75, ‐1.12]) and EAOASUVR‐WM (‐0.67 years; [‐2.13, 0.79]; Figure 2). EAOA variability was highest for DVR>1.8 where fewer longitudinal observations were available for model training. Conclusion SUVR with whole cerebellum as a reference region resulted in the smallest difference in EAOA compared to DVR from the SILA model. Future work is ongoing to define SUVR thresholds that will optimize agreement with DVR‐trained EAOA results for harmonization of temporal estimates.