Abstract
Alzheimer's disease (AD), the leading cause of dementia, could potentially be mitigated through early detection and interventions. However, it remains challenging to assess subtle cognitive changes in the early AD continuum. Computational modeling is a promising approach to explain a generative process underlying subtle behavioral changes with a number of putative variables. Nonetheless, internal models of the patient remain underexplored in AD. Determining the states of an internal model between measurable pathological states and behavioral phenotypes would advance explanations about the generative process in earlier disease stages beyond assessing behavior alone. Previously, Gershman et al., 2017b proposed the latent cause model, which provides a normative account of memory modification phenomena in Pavlovian fear conditioning. Here, we assumed the latent cause model as an internal model and estimated internal states defined by the model parameters being in conjunction with measurable behavioral phenotypes. The 6- and 12-month-old AppNL-G-F knock-in AD model mice and the age-matched control mice underwent memory modification learning, which consisted of classical fear conditioning, extinction, and reinstatement. The results showed that AppNL-G-F mice exhibited a lower extent of reinstatement of fear memory. Computational modeling revealed that the deficit in the AppNL-G-F mice would be due to their internal states being biased toward overgeneralization or overdifferentiation of their observations, and consequently, the competing memories were not retained. This deficit was replicated in another type of memory modification learning in the reversal Barnes maze task. Following reversal learning, AppNL-G-F mice, given spatial cues, failed to infer coexisting memories for two goal locations during the trial. We concluded that the altered internal states of AppNL-G-F mice illustrated their misclassification in the memory modification process. This novel approach highlights the potential of investigating internal states to precisely assess cognitive changes in early AD and multidimensionally evaluate how early interventions may work.