Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales

Abstract

Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limited evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in multiple temporal contexts, potentially creating interference. Here, using 7T fMRI, we measured CA1 and entorhinal activity patterns as human participants viewed thousands of natural scene images distributed, and repeated, across many months. We show that memory for an image's original temporal context was predicted by the degree to which CA1/entorhinal activity patterns from the first encounter with an image were re-expressed during re-encounters occurring minutes to months later. Critically, temporal memory signals were dissociable from predictors of recognition confidence, which were carried by distinct medial temporal lobe expressions. These findings suggest that CA1 and entorhinal cortex preserve temporal memories across long timescales by coding for and reinstating temporal context information.

Benefits of spaced learning are predicted by re-encoding of past experience in ventromedial prefrontal cortex

“…Our finding of spacing-related increases in vmPFC similarity is consistent with recent evidence in humans 18 and rodents 19 of increases in neural similarity with spacing at short timescales. Our finding that these increases predicted memory aligns with other empirical evidence that neural similarity across stimulus repetitions is positively related to memory 48–50 and challenges leading theories of spacing effects that emphasize the role of encoding variability. Thus, we were able to identify opposing effects of spacing on representational similarity (increases and decreases in similarity), but we specifically linked the behavioral benefits of spacing to increases in neural similarity.…”

“…Specifically, an abstraction account predicts that, with relatively long spacing, retrieval of the original encounter should actively weaken temporal memory for individual encounters. On the one hand, this prediction is challenged by existing behavioral 13 and fMRI evidence 50 indicating that retrieval of past encounters actually enhances temporal memory. On the other hand, it is notable that temporal memory does not necessarily benefit from spaced learning 50 .…”

“…On the one hand, this prediction is challenged by existing behavioral 13 and fMRI evidence 50 indicating that retrieval of past encounters actually enhances temporal memory. On the other hand, it is notable that temporal memory does not necessarily benefit from spaced learning 50 . Thus, in future work it will be of interest to further characterize the relationship between spaced learning and contextual memory (including temporal memory) as understanding this relationship will help refine theoretical accounts.…”

Structured memory representations develop at multiple time scales in hippocampal-cortical networks

“…Similar designs that extensively sample a small group of individuals have also been used across multiple fMRI studies, such that sufficient statistical power is obtained by extensively sampling individuals rather than sampling less frequently in a smaller population (Gordon et al, 2017;Gratton & Braga, 2021;Kragel et al, 2021;Laumann et al, 2015;Miller et al, 2022;Naselaris et al, 2021;Nee, 2019;Newbold et al, 2020;Popham et al, 2021). Moreover, the collection of multiple data points over time is critical to characterize memory processes that unfold at extended timescales (Miller et al, 2022;Vanasse et al, 2022;Zou et al, 2022).…”

“…Turning to schematic temporal position representations, ROI analyses revealed no reliable evidence of pattern strength change across all voxels in the left hippocampus (logistic fit, r = 0.07, p = 0.32; quadratic fit, 2 nd order coefficient = 3.8 x 10 -6 , r = -0.03, p = 0.80) or right hippocampus (logistic fit, r = -0.20, p = 0.96; quadratic fit, 2 nd order coefficient = -1.0 x 10 -6 , r = -0.03, p = 0.79). This null finding was unexpected, considering extensive evidence for temporal context representations in the hippocampus (Deuker et al, 2016;Ezzyat & Davachi, 2014;MacDonald et al, 2011;Naya & Suzuki, 2011;Zou et al, 2022). However, given established functional diversity in the hippocampus (Poppenk et al, 2013;Thorp et al, 2022;S.-F. Wang et al, 2016), it is possible that schematic temporal representations may develop in more localized 11 Data points show pattern strength (beta coefficients) for each participant relative to baseline (mean across first two sessions, prior to sequence exposure).…”