Longitudinal multi‐day learning curves (MDLCs) to capture subtle cognitive changes in preclinical Alzheimer’s disease

Background Remote digital testing provides the opportunity to deploy memory paradigms that mimic learning in everyday life by exposing participants to repeated stimuli over frequent intervals. Here, we used the Boston Remote Assessment for Neurocognitive Health (BRANCH) multi‐day learning curve (MDLC) paradigm and investigated whether repeating MDLCs over time could capture subtle cognitive changes in preclinical Alzheimer’s disease (AD). Method N = 223 cognitively unimpaired older adults (age = 74±8.1, 65% female, MMSE 29±1.4) with standardized cognitive testing and amyloid and tau PET from the Harvard Aging Brain Study completed a modified version of the Face‐Name Association Examination (FNAME) at‐home for seven consecutive days on a personal device. After 10.8±2.6 months a subsample (n = 54; age = 75±8.8, 61% female, MMSE = 29±1, 19% Aβ+, n = 51 with tau PET) completed a second version of the FNAME with new stimuli for seven consecutive days. A summary learning curve metric for each MDLC (baseline and follow‐up) was computed using an Area Under the Curve (AUC) method allowing for the combination of Day 1 performance and a learning trajectory over the subsequent six days. We used linear mixed effect (LME) models on the MDLC AUCs to investigate change in MDLCs and compared this to change in Day 1 performance of the MDLCs. We also ran LME models adjusting for demographic factors to examine whether baseline Preclinical Alzheimer’s Cognitive Composite‐5 (PACC5) performance and amyloid and tau burden were associated with change in MDLCs. Result Overall, MDLCs diminished over time (Time = ‐0.028, 95%CI[‐0.056 – ‐0.001], p = 0.047) which was not detected by change in Day 1 performance of the MDLC (Time = ‐0.026, 95%CI[‐0.063 – 0.012], p = 0.117). A lower MDLC at follow‐up compared to baseline was associated with elevated amyloid (Aβ+ Group*Time = ‐.08, [95%CI = ‐0.15‐ ‐0.01], p = 0.042) (Figure 1) and worse PACC5 performance (PACC5*Time = 0.05, 95%CI = [0.01‐0.09], p = 0.029) (Figure 2). Greater entorhinal tau burden was associated with a lower baseline MDLC (Tau = ‐0.18, 95%CI[‐0.34 – ‐0.03], p = 0.025) but not with MDLC change over time. Conclusion These results highlight how capturing multiple datapoints over days detects short‐term cognitive changes in preclinical AD that are undetectable using standard single timepoint assessments. Paradigms such as BRANCH MDLC may thereby offer more sensitive cognitive outcome measures for AD secondary prevention trials.