Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

Abstract

AbstractDespite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade and, without such a measure, it is hard to identify the corresponding neural correlates. This study addresses this problem using a combination of individual differences, model-based inferences, and resting-state functional connectivity. The individual-specific values of rate of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to data from an adaptive fact learning task. Individual rates of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine-learning techniques to identify the most predictive and generalizable features. Consistent with the existing literature, our results identified a sparse, distributed network of cortical and subcortical regions that underlies forgetting in LTM. Cross-validation showed that individual rates of forgetting were predicted with high accuracy (r = .96) from this connectivity pattern alone. These results open up new opportunities for the study of individual differences in LTM function and dysfunction.