Episodic memory retrieval success is associated with rapid replay of episode content

Abstract

Retrieval of everyday experiences is fundamental for informing our future decisions. The fine-grained neurophysiological mechanisms that support such memory retrieval are largely unknown. We studied participants who first experienced, without repetition, unique multicomponent 40–80-s episodes. One day later, they engaged in cued retrieval of these episodes while undergoing magnetoencephalography. By decoding individual episode elements, we found that trial-by-trial successful retrieval was supported by the sequential replay of episode elements, with a temporal compression factor of >60. The direction of replay supporting retrieval, either backward or forward, depended on whether the task goal was to retrieve elements of an episode that followed or preceded, respectively, a retrieval cue. This sequential replay was weaker in very-high-performing participants, in whom instead we found evidence for simultaneous clustered reactivation. Our results demonstrate that memory-mediated decisions are supported by a rapid replay mechanism that can flexibly shift in direction in response to task goals.

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Fig. 1: Experimental design and decoding of the episode elements.
Fig. 2: Mean sequenceness (replay) in the after condition, and the relationship between sequenceness and performance in the after and before memory retrieval conditions.
Fig. 3: Relationship between forward and backward sequenceness and trial-by-trial memory retrieval success in the after and before conditions.
Fig. 4: Beamforming analysis of power increases at the onset of sequenceness events and time–frequency analyses of replay onset.
Fig. 5: The relationship between cue-evoked reactivation and performance.

Data availability

Complete behavioral data are available on the Open Science Framework (https://osf.io/qaewv/). Unthresholded group beamforming statistical parametric maps of replay onset power changes and category responses during the localizer are available on NeuroVault (https://neurovault.org/collections/6088/). The raw MEG data are available on OpenNeuro (https://openneuro.org/datasets/ds002761).

Code availability

Code for the sequenceness analysis, as included in the full processing pathway simulation, can be found in the Supplementary Software and is also available on GitHub (https://github.com/gewimmer-neuro/memory-sequences).

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Acknowledgements

The authors thank Z. Kurth-Nelson for helpful discussions. This work was supported by a Wellcome Trust Investigator Award (098362/Z/12/Z) to R.J.D. G.E.W. was supported by Max Planck Society (647070403019). Y.L. is supported by a UCL Graduate Research Scholarship and an Overseas Research Scholarship. The Max Planck University College London Centre is a joint initiative supported by University College London and the Max Planck Society. The Wellcome Centre for Human Neuroimaging is supported by core funding from the Wellcome Trust (203147/Z/16/Z).

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Authors

Contributions

G.E.W., Y.L. and N.V. designed the experiment. G.E.W. and N.V. collected the data. G.E.W. and Y.L. wrote the analysis code, and analyzed and interpreted the data. T.E.J.B. and R.J.D. contributed to data interpretation. G.E.W. wrote the paper with input from Y.L., N.V., T.E.J.B. and R.J.D.

Corresponding author

Correspondence to G. Elliott Wimmer.

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Extended data

Extended Data Fig. 1 Memory performance as a function of episode length and episode ending and performance on the post-scan episode re-ordering test.

As in Fig. 1c, purple dots represent individual data points for regular performance participants with sufficient incorrect response (error) trials free from MEG artifacts for accuracy analyses (after, n = 17; before, n = 18), in order to keep consistency between behavioral and MEG analyses. Note that performance for participants in subsets of trials will not always follow the mean defined by all trials. a and b, Memory did not significantly differ in the after condition by length (one-sample two-sided t-test t(24) = -1.389; P = 0.178; two one-sided test (TOST) equivalence test P = 0.065, thus we are unable to rule out the presence of a medium-sized effect) or the before condition by length (t(24) = 0.661; P = 0.515; TOST equivalence test P = 0.0156). c and d, Memory did not differ in the after condition by end valence (t(24) = -0.068; P = 0.946; TOST equivalence test P = 0.004) or the before condition by end valence (t(24) = 0.1478; P = 0.88; TOST equivalence test P = 0.005). Given the null behavioral differences, MEG analysis collapsed across these variables. e, Performance on the post-scan episode sequence memory re-ordering test (n = 24 participants; data are missing from one participant). Participants attempted replacement of each element within each episode; individual scores represent average binary accuracy. Sequence memory did not have a condition, so regular performance participants (purple) represent those participants included in both the after and before condition regular performance groups (n = 15); the data points for the remaining high performance participants are depicted in orange. Gray boxes represent the 25–75 percentiles of the data; median: line, mean: dot; error bars represent standard error of the mean (s.e.m.). All figure results include n = 25 participants.

Extended Data Fig. 2 Illustration of localizer classifier performance for the six stimulus categories that made up the first 4 components of episodes and performance of a classifier trained for each of the 32 individual stimuli from these categories.

a, Cross-validated classification performance for each category (face, building, body part, object, animal and car). Results represent training on the 200 ms time point and testing across all time points. b, Classifier sensor weight correlations across participants within and between-categories reveal strong within-category similarity, suggesting similar sensor importance across participants for the same categories. Categories are sorted as in the legend for panel a: face, building, body part, object, animal and car. c, Classification performance for each of 32 stimuli. Results represent training on the 200 ms time point and testing across all time points. (Colors were randomly assigned.) d, Cross-classification performance for 32 individual stimuli where the classifier was trained on the localizer phase (at 200 ms) and tested on the cues in the memory phase. Performance was above the pre-trial baseline level and significant (one-sample two-sided t-test t(24) = 9.20, P<1e-8). Compared to the category-level cross-classification results in Fig. 1f (y-axis range is matched across figures for comparison), the magnitude of the effect versus baseline was weaker: the effect for the category across-phase classification of memory cues was significantly stronger than the single-stimulus across-phase classification (t(24) = 12.47, P<1e-11). Yellow line represents 200 ms, the peak category classification time point in the localizer phase. The dashed line represents the estimated chance threshold to assess significance. e, Trained classifier beta weight correlation across sensors across all 32 individual stimuli depict natural emergence of category structure. The image represents that average of individual participant correlation matrices. Categories are sorted as in the legend for panel a: face, building, body part, object, animal and car. Shaded error margins represent s.e.m.. All figure results include n = 25 participants.

Extended Data Fig. 3 Illustration of source localization results for the six categories of stimuli in the localizer phase.

Each category was contrasted versus all other categories. We found expected patterns of activation for the 4 categories that have received the most investigation in the literature: faces, buildings, body parts, and objects. For faces, activation peaked in a region roughly consistent with the fusiform face area (FFA) as well as the occipital face area (OFA). Activation for building stimuli was located between the well-known parahippocampal place area (PPA) and the retrosplenial cortex (RSC), a region also known to respond to scene and building stimuli. Activation for body part stimuli was in a region consistent with the extrastriate body area (EBA). Activation for objects was in a region consistent with the object-associated lateral occipital cortex (LOC) as well as an anterior temporal cluster that may relate to conceptual processing of objects. Activity for the two less-studied categories, animals and cars, was localized to different areas of the ventral and posterior occipital cortex. Each individual category was thresholded at a level that best displayed localized peaks. Results are for illustration only. Full unthresholded maps can be found at https://neurovault.org/collections/6088/. The z coordinates refers to the MNI atlas. All figure results include n = 25 participants.

Extended Data Fig. 4 Sequenceness analysis schematic.

a, Classifiers were trained on the 6 categories that made up the episodes. The mean weighting (approximate importance) of each sensor for a given category, for illustration; in analyses, participant-specific weights were used. (Anterior = top; posterior = bottom.) b, Mean sensor weighting across all categories. (In panels a and b, results represent the mean across n = 25 participants.) c, Illustration of how the trained classifiers are applied to the MEG data time series for each cued retrieval period, where states 1-4 represents episode components 1-4 from Fig. 1a. d, The sequenceness analysis detects systematic time shifts (T) in category evidence. A forward sequence illustration is shown on the left; a backward sequence illustration is shown on the right.

Extended Data Fig. 5 Results of permutations and simulations of the complete MEG analysis pipeline.

The top row shows the percentage of resulting P-values from 0-5% derived from the individual differences analyses and the bottom row shows the percentage of resulting P-values from 0-5% derived from the trial-by-trial multilevel regression analyses (panels d-f). After condition in cyan; before condition in blue. In panel a and d, ‘x’ symbols mark the empirical 5% thresholds noted in the Results. a, Permutation results of the individual differences analysis using real MEG and behavioral data, shuffling correct and incorrect labels (25k simulations). b, Results using simulated MEG data of the individual differences analyses with behavioral variables taken from actual participant data (10k simulations). c, Results using simulated MEG data of the individual differences analyses with simulated (approximated) behavioral variables (10k simulations). d, Permutation results of the trial-by-trial multilevel regression analysis using the real MEG and behavioral data, shuffling correct and incorrect labels (25k simulations). e, Simulation results of the multilevel regression analyses with behavioral variables taken from actual participant data (10k simulations). f, Simulation results of the multilevel regression analyses with simulated (approximated) behavioral variables (10k simulations). All simulations include n = 25 participants.

Extended Data Fig. 6 Additional individual differences analyses using true sequenceness and other episode sequenceness.

As in Fig. 1c the data points for the regular performance participants are shown in purple; high performance participants are shown in orange. Correlations report two-sided Pearson’s correlation. a,b, Mean performance relationship to sequenceness, as in Fig. 3, but here separating sequenceness into forward and backward components. (a) In the after condition, a regression analysis including both forward and backward sequenceness showed that reverse sequenceness was negatively and numerically more strongly related to mean performance (forward, one-sample two-sided t-test, t(22) = 2.265, P = 0.0337; backward t(22) = -2.9111, P = 0.0081). Forward in purple; backward in blue. (b) In the before condition, forward sequenceness was related to mean performance (forward, one-sample two-sided t-test, t(22) = -2.2419, P = 0.0354; backward t(22) = 1.0456, P = 0.3071). Error bars represent 95% CIs. Both results support the primary individual difference correlations in the Results (Fig. 3). c-d, In the after and before conditions, mean sequenceness strength (forward-backward) with a 40–50-ms lag did not significantly relate to overall mean memory performance (percentage of correct trials). (d) As in panel c, here for the before condition. e-h, Correlations with the ‘other episode’ sequenceness measure. (e,f) In the after and before conditions, mean 40-50 ms sequenceness for other episode transitions (excluding the current episode) did not significantly relate to mean memory performance. (g-h) In the after and before conditions, mean 100–120-ms sequenceness for other episode transitions did not significantly relate to mean memory performance. All figure results include n = 25 participants.

Extended Data Fig. 7 Analyses relating both current episode sequenceness and ‘other episode’ sequenceness to accuracy.

Forward in purple; backward in blue. Multilevel regression analysis beta coefficients in panels a-f and h; two-sided p-values determined via bootstrapping. Error bars represent 95% CIs. a, In the after condition, sequenceness derived from current episode sequenceness (100–120-ms; left) remained significant while other episode sequenceness at lags determined by the leave-one-subject-out (LOSO) procedure showed no relationship to successful retrieval (right; Supplementary Table 3). b, As in panel a, but for the before condition. c, Difference between current episode sequenceness (LOSO lag) and other episode sequenceness in the after condition (left) and before condition (right). d, In the after condition, current episode sequenceness (100–120-ms lag; left, darker color) remains significant (left) while other episode sequenceness (derived from all other transitions excluding the current episode transitions) at a lag of 40–50-ms lag showed no relationship to successful retrieval (right, lighter color; Supplementary Table 4). e, As in panel d, but for the before condition. f, Difference between current episode sequenceness (40–50-ms lag) and other episode sequenceness in the after condition (left) and before condition (right). g, Individual regression coefficients for the trial-by-trial relationship between sequenceness and successful retrieval in the after and before conditions as in Fig. 3a., but derived from a single-level GLM instead of a multilevel regression analysis. (Gray boxes represent the 25–75 percentiles of the data; median: line, mean: dot; error bars represent s.e.m. h, For a single stimulus analyses (instead of the primary category-level analyses), the classifier showed cross-classification performance that was numerically close to chance (see Extended Data Fig. 3). For completeness, in the after condition, we found no relationship between single-stimulus backward sequenceness and retrieval success (P = 0.379). In the before condition we found a positive, but non-significant, relationship between single-stimulus forward sequenceness and retrieval success (P = 0.0920). All figure results include n = 25 participants. *P < 0.05.

Extended Data Fig. 8 Additional sequenceness onset beamforming results.

a, Timecourse of power changes relative to replay onset in the anterior hippocampus in the after (cyan) and before (blue) conditions. (Shaded error margins represent SEM.) b, Power in the right visual cortex at replay onset in the after condition, displaying a different view of the whole-brain results shown in a coronal section in Fig. 4a. Statistical map thresholded at P < 0.001 uncorrected, for display; cluster significant at P < 0.001 whole-brain FWE, one-sided. c, Power in the left MTL 10 ms before the onset of reverse sequenceness events correlated with performance, such that lower performing participants showed the strongest increase in power (https://neurovault.org/images/306232/). Statistical map thresholded at P < 0.001 uncorrected, for display; cluster significant at P = 0.003 whole-brain FWE, one-sided. d, Performance – power relationship in the right anterior hippocampus. Data are for visualization purposes only and represent the peak coordinate as in panel c. High performance participants in orange; regular performance participants in purple. e, After condition unthresholded backward replay event onset beamforming results. f, Before condition unthresholded forward replay event onset beamforming results. For panels e and f, transparency shows underlying anatomy; maximum color P = 0.0001. All figure results include n = 25 participants. The x, y, and z coordinates refers to the MNI atlas.

Extended Data Fig. 9 Time-frequency analysis of replay onsets in the after and before conditions separately.

Results as in Fig. 4c, d. a, Time-frequency analysis showing power increases at replay onset in the after condition showing frequencies up to ~50-Hz. 0 ms represents the onset of putative replay events. (Average across correct trials.) b, Time-frequency analysis as in panel a, here in the before condition. c, Time-frequency analysis of high frequencies in the after condition (using data sampled at 600-Hz) relative to replay onset d, Time-frequency analysis of high frequencies as in panel c, here in the before condition. All figure results include n = 25 participants.

Extended Data Fig. 10 Relationship between accuracy and alternative non-sequential measures: cue-evoked reactivation, on-screen cue response, and zero-lag correlation between within-episode category evidence during the retrieval period.

Forward in purple; backward in blue; control variable in red. Regression coefficients from multilevel analyses; two-sided p-value determined via bootstrapping. Error bars represent 95% CI. a-b, Cue-evoked reactivation of within-episode elements minus other-episode elements from 200–250-ms and retrieval success in the after condition (a) and before condition (b), included in the regression model with forward and backward sequenceness. The effects of cue-evoked reactivation were non-significant (Supplementary Table 7); the relationships between sequenceness and memory were unaffected. c-d, Evidence for response to the on-screen cue from 200–250-ms and retrieval success in the after condition (c) and before condition (d), included in the regression model with forward and backward sequenceness. The effect of cue response was significant in the after condition but not the before condition (Supplementary Table 7); the relationships between sequenceness and memory were unaffected. e, The correlation between within-episode category evidence across the 160–3667-ms cue period is driven by high-magnitude correlation events (>= 95% of mean), and activity for these events peaks and falls rapidly. For illustration, the purple line represents the mean across participants in the after condition. (Shaded error margins represent s.e.m.) f, The correlation between evidence for within-episode categories minus the correlation between all other pairings (zero-lag correlation difference) across the 160–3667-ms cue period of analysis is not related to mean performance on a given episode (as in Fig. 5c) in very high or regular performance participants: High performance (multilevel regression β = -0.050 ± 0.0463; z = -1.079, P = 0.290; n = 10 participants); regular performance (β = -0.0612 ± 0.0590; z = -1.037, P = 0.296; n = 15 participants). g-h, The zero-lag correlation difference included in the regression model with forward and backward sequenceness in the after condition (g) and in the before condition (h). The effect of clustered reactivation was non-significant (after: β = 0.118 ± 0.462; z = 0.255, P = 0.770; before: β = -0.724 ± 0.494; z = -1.467, P = 0.145); the relationships between sequenceness and memory were unaffected. Error bars represent 95% CIs. With the exception of panel f, all figure results include n = 25 participants.

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Wimmer, G.E., Liu, Y., Vehar, N. et al. Episodic memory retrieval success is associated with rapid replay of episode content. Nat Neurosci 23, 1025–1033 (2020). https://doi.org/10.1038/s41593-020-0649-z

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