Large-Scale Longitudinal Assessment of Declining Memory Function Through Individualized Modeling of Episodic Memory

Abstract

The detection and tracking of progressive memory impairments, particularly in the context of neurodegenerative disorders, relies predominantly on traditional neuropsychological assessment and short cognitive screening tools. These methods, however, are resource-intensive and lack the accessibility and/or the repeatability necessary for effective early identification and tracking interventions. This study addresses the critical need for reliable and efficient diagnostic tools to track and predict memory decline in clinical settings. We demonstrate that an online, remote model-based memory assessment, can identify individuals with Mild Cognitive Impairment (MCI) with an accuracy rate exceeding 84% in a single 8-minute session. Furthermore, the test can be repeated multiple times with increasing accuracy over multiple assessments. The system's ability to monitor individual memory function inexpensively and longitudinally across various materials offers a robust and repeatable alternative to the static measures currently employed. Our findings show that traditional methods to assess memory decline could be replaced by adaptive, precise, and patient-friendly online tools based on computational modeling techniques. Moreover, our findings also open avenues for the proactive management of Alzheimer's disease and other dementias, as well as sensitively tracking the effect of interventions in early disease.