[P1–122]: WHAT IS MEMORABLE IS CONSERVED ACROSS HEALTHY AGING, EARLY ALZHEIMER's DISEASE, AND NEURAL NETWORKS
2017-07-01

Recent work finds that memorability is a measurable, consistent, and intrinsic image property (Isola et al., 2011; Bainbridge et al., 2013) that triggers specific neural patterns in memory-related brain regions (Bainbridge et al., in press). In healthy populations, people tend to remember and forget the same images, regardless of their own unique experiences. Image memorability can also be predicted using convolutional neural networks (CNN). However, image memory declines with age and early Alzheimer's Disease (Subjective Cognitive Decline, SCD; Mild Cognitive Impairment, MCI). It is unclear whether there are still images that are consistently remembered or forgotten and what image types are specifically prone to forgetting in early disease stages. Image-driven memorability effects were analyzed from a scene memory paradigm (Düzel et al., 2011) from the DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study, including healthy elderly participants (N=148), SCD patients (N=91), and MCI patients (N=47). Participants saw 88 indoor and outdoor scene images (2.5s presentation). After 85 min, participants performed a recognition task (44 foil images) indicating memory confidence (1–5). 835 images were seen across all participants and memorability was determined as the hit rate (HR) in this task. We used the LaMem CNN (Khosla et al., 2015), trained on memory performance from young healthy adults, to predict memorability scores for the same images. We also investigated features that determine memorability across patient groups. As expected, HR declined with disease severity (healthy M=0.67, SCD M=0.64, MCI M=0.55). However, image memorability ranking was significantly correlated between healthy elderly participants and patients with SCD (r=0.50, p<10-53) and MCI (r=0.26, p<10-13), Fig. 1–2. CNN memorability predictions (Fig. 3) were also significantly correlated (healthy: r=0.25, p<10-12; SCD: r=0.23, p<10-10; MCI: r=0.08, p<0.05). These results reveal an impressive consistency to what patients with SCD and MCI remember, matching what is remembered by the general population as well as predictions from neural networks. Memory loss in these patient groups for specific images may thus be partially guided by the images’ intrinsic memorability, and could be predicted from healthy participants or neural networks to guide future diagnostic and therapeutic tools. Example memorable (top) and forgettable (bottom) images across all participant populations. Numbers indicate memorability score (HR) for that group (ranging from 0 to 1). (Left) Scatterplot of memorability scores for healthy elderly participants (x-axis) compared to SCD patients (y-axis). As one can see, there is a significant correlation in what images are memorable and forgettable between these groups (r = 0.50, p < 10-53). (Right) Scatterplot of memorability scores for healthy elderly participants (x-axis) compared to MCI patients (y-axis). Memorability here is also significantly correlated (r = 0.26, p < 10-13) An example of the LaMem Convolutional Neural Network (Khosla et al., 2015) memorability prediction for an image. The heatmap indicates which portions of an image contribute to its ultimate memorability score, with more memorable portions in red (e.g., the people) and less memorable portions in blue (e.g., the ceiling lights).