Recent evidence challenges the widely held view that the hippocampus is specialized for episodic memory, by demonstrating that it also underpins the integration of information across experiences. Contemporary computational theories propose that these two contrasting functions can be accomplished by big-loop recurrence, whereby the output of the system is recirculated back into the hippocampus. We use ultra-high-resolution fMRI to provide support for this hypothesis, by showing that retrieved information is presented as a new input on the superficial entorhinal cortex—driven by functional connectivity between the deep and superficial entorhinal layers. Further, the magnitude of this laminar connectivity correlated with inferential performance, demonstrating its importance for behavior. Our findings offer a novel perspective on information processing within the hippocampus and support a unifying framework in which the hippocampus captures higher-order structure across experiences, by creating a dynamic memory space from separate episodic codes for individual experiences.

We focused on inference test trials (i.e., AC trials) in which this circuit-level mechanism makes specific predictions ( Figure 1 B). First, during AC test trials requiring the integration of information across episodes (e.g., AB and BC), we should see evidence of reactivation of the unseen item (i.e., B) on the input layer (sEC) due to recirculation from the output (dEC). Second, we should see evidence for functional connectivity between these layers, reflecting the active recirculation of information. Third, the magnitude of recirculation through this pathway connecting the output layer (dEC) to the input layer (sEC) should predict individual variation in inference performance. Critically, recirculation of the output of the hippocampal system as a new input can only exist in the presence of a big-loop mechanism, and therefore the predictions outlined above contrast with those made by encoding-based models (e.g.,; also see Figure S2 ). Specifically, encoding-based models are consistent with reactivation in the dEC of the shared component across related memories (i.e., the B item, shared across AB and BC pairs in the PAI task) as part of pattern completion of a composite/blended representation (i.e., ABC; Figure S2 ). However, they do not predict recirculation of this information from the dEC to the sEC mediated by connectivity between these layers, or the functional relevance of this mechanism to behavior.

In contrast to retrieval-based big-loop recurrent models, encoding-based models () implicitly de-emphasize the notion of pattern separation—a computation viewed to be critical to episodic memory ()—and instead suggest that it is the overlap of hippocampal neural codes for related experiences that is critical to inferential behavior (see Figure S2 for details). This account is supported by several studies (), indicating that encoding-based mechanisms can support inference when the stimuli have been repeated several times, giving an opportunity for the integrated representations to be formed within the hippocampus (consistent with simulated results from). Thus, while this encoding-based contribution has been experimentally established, the proposed big-loop mechanism has yet to be empirically validated. Therefore, the aim of the current experiment was to test the hypothesis that the hippocampal system functions as a big-loop recurrent circuit that enables the recirculation of information from the superficial (sEC) and deep (dEC) layers of the EC ( Figure S1 ; also see). To achieve this, we employed the PAI task ( Figure 1 A;)—a prototypical setting in which to examine the mechanisms underlying the ability to integrate information across episodes—together with ultra-high-resolution multivariate fMRI techniques, which allowed us to dissociate neural activity in the input and output layers of the hippocampal system: the sEC and dEC layers (), respectively ( Figure S3 STAR Methods ).

As an everyday example of the dynamic interplay of distinct yet related memories mediated by big-loop recurrence, imagine meeting a work colleague (Alex) and suddenly remembering meeting them for the first time in a conference in Paris the year before. This then triggers a strong memory of a romantic trip to Paris with a partner (Sam) many years previously, which itself triggers another memory of a holiday spent together in Thailand. In this example, this process effects a form of “memory chaining” whereby individual elements of episodic memories can trigger the retrieval of other memories from completely different times of our lives. The big-loop recurrence account proposes that the hippocampus represents related experiences (e.g., Alex-Paris and Paris-Sam in the example above) through pattern separated codes, and that recirculation of shared components across experiences as the output back into the hippocampus (i.e., Paris) allows the retrieval of other related memories (Paris-Sam; Sam-Thailand). While the example above illustrates how the algorithm allows memories to be chained together, more generally it mediates a successive search through memory with subsequent retrieval conditioned on the products of previous retrieval resulting in an iterative process consisting of cycles of memory retrieval and re-querying of the hippocampus (). This provides a powerful mechanism for generalization and inference, as exemplified by the widely studied PAI task ( Figure 1 A), as it effectively allows the recombination of related episodes at the point of retrieval, thereby allowing appreciation of the commonalities and higher-order structure present among the set of experiences (; also see).

What representations and computations underpin the contribution of the hippocampus to generalization and inference?.

Recently developed computational theories (; also see) have posited a novel mechanism that resolves the tension between episodic memory and integration across episodes. Specifically, the hippocampal system is proposed to act as a big-loop recurrent circuit, whereby the output of the system is recirculated as a new input rather than constituting the endpoint of hippocampal processing (). Notably, the use of the term big-loop recurrence (), which supports the appreciation of structure across episodes, serves to distinguish this mechanism from the well-established internal recurrence within the CA3 region of the hippocampus (), which supports within-episode pattern completion ( Figure S1 ). Anatomically, recirculation mediated by big-loop recurrence could be enabled by direct projections between the input and output layers of the entorhinal cortex (EC), which have been documented in rats () and monkeys (), or by a wider circuit involving additional neocortical regions (). By this algorithmic account, the hippocampus represents related experiences (e.g., AB and BC in the paired associate inference [PAI] task; Figure 1 A) through pattern separated codes: big-loop recurrence effectively allows the recombination of related episodes at the point of retrieval, thereby allowing appreciation of the commonalities and higher-order structure present among the set of experiences ( Figure 1 B;; also see).

(B) Schematic illustration of how big-loop recurrence supports performance on AC test trials. The first pass results in hippocampal memory retrieval and updating of the representation on the output (dEC) layer (i.e., activation of the representation corresponding to B 1 ). Next, the product of the initial cycle of retrieval is recirculated from the output layer (dEC) to the input layer (sEC). Then in the second pass, the updated input layer representation on the sEC, which consists of the initial query and the product of the previous cycle of retrieval, re-queries the hippocampus. Following subsequent cycles involving an interplay between feature space and episode space, this results in the network producing the correct answer (C 1 ).

(A) Experimental design. 144 triplets (i.e., A n B n C n , where n = 1:144) were used as stimuli in which A and C were always faces, linked by a scene or object (B). After two training exposures (AB and BC pairs presented separately), subjects were tested on direct associations (AB/BC test trials) and indirect associations (AC test trials). During inference test trials (i.e., AC) only faces were displayed, allowing us to investigate reactivation of retrieved memory content (connecting B item) by decoding scenes versus objects from the neural activation. Note that lures (i.e., C 3 in illustrated test trial) were associated with the same category (object/scene) as the target. The star indicates the correct answer (C 1 ). Note that no feedback was given.

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

The hippocampus is widely accepted to be critical to episodic memory, by creating orthogonalized representations that minimize interference between experiences (). Emerging evidence, however, suggests that the hippocampus also plays an important role in integrating information across episodes (), a function that exposes a fundamental tension with its role in pattern separation ()—a computation that inherently disregards the commonalities between episodes (). Recent work, therefore, presents a substantial challenge to theoretical frameworks that view the hippocampus as being specialized for episodic memory (), with the gradual extraction of statistical structure across experiences relying on the neocortex ().

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

What representations and computations underpin the contribution of the hippocampus to generalization and inference?.

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

Having established the existence of the predicted functional pathway, we next turned to the final prediction of our model—that big-loop recirculation should predict behavioral inference performance. To test this prediction, we again leveraged the individual variation in our data and correlated the strength of laminar IC with average performance on the AC test trials ( Figure 5 C). These variables were significantly positively correlated (r = 0.378, p = 0.028), indicating that the degree of recirculation has an impact on inference ability. By contrast, dEC reactivation, which we use as a proxy for the strength of conventional associative retrieval of the AB and BC pairs through pattern completion elicited by the presence of A and C items on the screen during AC test trials, did not significantly correlate with behavioral AC inference performance (r = −0.197, p = 0.833). Indeed, a direct comparison revealed that the correlation with AC test performance was significantly higher with laminar IC compared to dEC reactivation (difference in r = 0.574, p = 0.021). Additional control analyses demonstrated that these results could not be accounted for by any confounding correlations between the laminar IC and dEC reactivation (r = −0.133, p = 0.736; Table S4 ), or by additional pathways running through the PHC/PRC ( Table S4 ). To further assess the laminar-specific contributions to behavior, we correlated AC inference performance with both sEC and dEC reactivation strength. If AC inference performance is selectively driven by big-loop recurrence passing information to the sEC, we would expect to see a significantly higher behavioral correlation with sEC than dEC reactivation. This was the case both with (difference in r = 0.606, p = 0.017) and without (difference in r = 0.481, p = 0.045) partialling out the reactivation strength in the opposing layer. This difference was driven by a marginally positive relationship between AC inference performance and sEC reactivation (while partialling out dEC, r = 0.337, p = 0.05; without partialling out dEC, r = 0.284, p = 0.079). By contrast, the dEC was not positively correlated with AC inference performance either with (r = −0.27, p = 0.9) or without (r = − 0.197, p = 0.831) partialling. This pattern of results was still present even when additionally partialling out the IC from PRC and PHC (AC performance and sEC reactivation, r = 0.337, p = 0.058; AC performance with dEC reactivation, r = −0.27, p = 0.895; difference, r = 0.607, p = 0.022). These results suggest that inference is dissociably related to information in the entorhinal input layers. Put together, these results reveal a direct correspondence between the strength of big-loop recirculation and overall inference performance across subjects, which could not be accounted for by variations in general retrieval strength. This is striking evidence in support of the unique functional role of the big-loop mechanism in episodic inference.

The results presented above point toward the conclusion that the laminar connectivity identified reflects a direct functional pathway between the dEC and sEC, rather than a larger pathway mediated by MTL regions such as the perirhinal and or parahippocampal cortex (PHC or PRC). To examine this further in the current context of recirculation of information back into the hippocampus, we also considered an alternative model of information flow incorporating a longer cortical loop through the PRC or PHC. Contrary to the predictions of this alternative model, we did not find a significant correlation between sEC reactivation and sEC/PRC IC (r = −0.083, p = 0.69) or sEC/PHC IC (r = 0.06, p = 0.776), nor was there a significant correlation between sEC/DG&CA3 and sEC/PRC (r = 0.207, p = 0.306) or sEC/PHC (r = −0.078, p = 0.713) IC. Thus, these results do not provide support for any longer cortical pathway, and are instead consistent with information flowing directly from the dEC to the sEC. Further, we ran each analysis outlined above, additionally partialling out both the sEC/PRC and sEC/PHC IC. Even with these extra controls, each analysis was still significant ( Table S3 ), demonstrating that they are not influenced by any extraneous signals arising in PRC or PHC.

One further prediction of the big-loop hypothesis is that information recirculated from the dEC to the sEC should then propagate back into the hippocampus—specifically into subregions viewed to instantiate pattern separated representations for individual experiences (“episode space”; Figure S2 ), namely the dentate gyrus (DG) and CA3 ( Figure S1 )—thereby allowing retrieved content to requery related experiences stored in these regions (). We tested this prediction using the same individual variation data, by correlating the laminar IC strength with the average IC strength between the sEC and the DG&CA3. This revealed a significant positive correlation (r = 0.391, p = 0.026), further supporting the hypothesis that the big-loop pathway drives recirculation of information back into the hippocampus. As each of these connections shares the sEC in common, they are not fully independent, which could artificially inflate the correlation. We therefore assessed a baseline control correlation between laminar IC and the average IC strength between the sEC and CA1&SUB. While these hippocampal subfields do share an anatomical connection with the sEC, these connections should not form part of the big-loop recurrent pathway (). Thus, if the sEC to DG&CA3 IC reflects the predicted operation of the big-loop pathway, the strength of this connection should be significantly greater than IC between the sEC and CA1&SUB. Notably, these latter connections share the same statistical dependence on the sEC, and therefore provide a stringent baseline. Further, this comparison provides a strong test of the predicted pathway from the sEC back into the hippocampus. Laminar IC did not significantly correlate with the sEC/CA1&SUB IC strength (r = −0.214, p = 0.82), and crucially, it was significantly weaker than the correlation between laminar IC and the sEC/DG&CA3 pathway (difference in r = 0.605, p = 0.016). These results clearly support the hypothesis that information is recirculated from the dEC to the sEC back into the hippocampal subfields CA3 and DG during performance of AC inference test trials.

What representations and computations underpin the contribution of the hippocampus to generalization and inference?.

As predicted, we found a significant positive correlation between the sEC reactivation strength and laminar IC ( Figure 5 ; r = 0.406, p = 0.018; one-tailed permutation tests used for all across-subject correlations). By contrast, the dEC was not significantly correlated with laminar IC (r = −0.202, p = 0.838), and a direct comparison of the two revealed that the sEC was significantly more highly correlated with laminar IC than the dEC (difference in r = 0.607, p = 0.014). This provides evidence consistent with the hypothesis that selective laminar connectivity is indeed driving the sEC reactivation. Note that this difference cannot be explained by a generic difference in signal intensity between the layers. There is no significant difference in CNR between the two layers, and tSNR is stronger in the dEC than in the sEC, which would predict a bias in sensitivity in the opposite direction to our results (see Figure S6 for details). To ensure that these correlation results were not influenced by the positive correlation between the sEC and dEC reactivation strengths (r = 0.199, p = 0.168), we ran a further partial correlation analysis in which for each correlation we controlled for reactivation strength in the other layer. This analysis revealed the same pattern of result (sEC, r = 0.464, p = 0.009; dEC, r = −0.315, p = 0.939; difference in r = 0.779, p = 0.003), demonstrating that the relationship between laminar IC strength and sEC reactivation cannot be explained by confounding correlations with the dEC. Notably, the finding of a clear dissociation between the entorhinal layers result provides additional strong evidence against the data being driven by vasculature confounds, as an effect that is uniquely attributable to the sEC cannot be driven by spurious BOLD confounds deriving from the dEC.

(C) Laminar connectivity correlates with behavioral AC (inference) performance ( r = 0.378 , p = 0.028 ; y axis displays proportion of correct responses). These results support the functional relevance of the entorhinal laminar connection underpinning big-loop recurrence, and its role in reactivation of memory content on the input layer of the EC (sEC), the re-entry of that information into the hippocampus, and its relevance for behavioral inference performance. All correlations have one-tailed permuted p values.

(B) Laminar connectivity correlates with sEC/DG&CA3 connectivity ( r = 0.391 , p = 0.026 ; y axis displays averaged connectivity in Spearman’s rho), which reflects the input pathway from the sEC back into the hippocampus.

(A) Across subjects, laminar connectivity correlates with sEC reactivation of memory content (r = 0.406, p = 0.018; y axis displays proportion of correctly classified trials; x axis displays averaged connectivity in Spearman’s rho in all panels).

Having established that the sEC contains reactivated information at the time of inference, and that there is a selective functional connectivity between the entorhinal layers, we next tested whether the sEC reactivation is driven by information flowing through this “big-loop” pathway, in line with our hypothesis ( Figure 1 ). We leveraged the individual variation in reactivation and IC strength in order to address this question. If information flows from the dEC to the sEC, then we would expect to see a positive correlation between laminar IC strength and sEC reactivation strength. Further, big-loop recirculation predicts that the correlation between the magnitude of laminar IC and reactivation strength in the input layer (sEC) should be significantly stronger than with the output layer (dEC). This is because the strength of sEC reactivation should depend specifically on big-loop recurrence indexed through laminar IC, whereas the strength of dEC reactivation likely reflects the more conventional mechanism of simple associative retrieval in which a subject retrieves the individual associate pairs AB or BC from the A and C items presented on the screen during AC test trials. Figure S8 displays simulation results confirming that, when individual differences in these two processes are independent, we should find differential correlations between laminar IC and sEC versus dEC reactivation.

We ran two additional analyses to ensure that this connectivity result could not be attributed to spurious local laminar correlations driven by the cortical vasculature. The first control analysis used the same procedure outlined earlier (i.e., in relation to the multivariate classification analysis), whereby the local BOLD signal from the neighboring dEC layer was removed from the sEC on a voxel-wise basis and vice versa ( STAR Methods ). Laminar IC was recalculated using this processed data, and was still found to be significantly stronger than the indirect MTL connectivity (z = 2.37, p = 0.009, one-sided; Figure S5 ). The second control analysis parcellated the EC layers each into two distinct sections (medial and lateral; Figure S5 ), and IC was recalculated between the layers across these distinct sections. There are known to be long-range anatomical connections between the layers, such that information could still in theory pass between these medial and lateral sections (), but in this case there can be no contribution from any local vascular artifacts. Laminar IC across the medial and lateral sections (i.e., medial sEC with lateral dEC, and lateral sEC with medial dEC) was significantly stronger than the indirect MTL connections (z = 2.07, p = 0.019, one-sided; Figure S5 ). These two control analyses provide evidence that there is a genuine selective functional connection between the EC layers (i.e., laminar connectivity) that cannot be accounted for by local hemodynamic contributions such as draining vein artifacts.

To validate that this laminar IC measure reflects a meaningful functional connection between the EC layers, we leveraged the well-established anatomical connectivity of the MTL using what we refer to as an “MTL IC direct/indirect analysis.” If IC is detecting meaningful functional connections, we should find that IC is stronger between regions that are directly anatomically connected than between regions that are only indirectly connected (i.e., are only connected via other regions, as part of a larger circuit). To accomplish this, we calculated the IC between every pair of regions in the MTL ( STAR Methods ). We then separated the IC measures into pairs of regions that are known to be directly anatomically connected, and those that are only indirectly connected ( Figure 4 D), leaving aside the laminar connection. These two sets of ICs were then averaged per subject to provide a summary of the “direct” and “indirect MTL” connectivity strength. Both showed a significant positive IC strength (direct, mean rho = 0.096 ± 0.002, z = 4.46, p < 0.0001; indirect, mean rho = 0.065 ± 0.003, z = 4.46, p < 0.0001, one-tailed), but crucially, the direct connectivity was significantly stronger than indirect connectivity (z = 4.07, p < 0.0001, one-tailed), showing that the strength of IC reflects the underlying functional connectivity of the region ( Figure 4 E). This comparison therefore allowed us to investigate whether the selective IC we find between the entorhinal layers is more similar to a “direct” MTL connection than an “indirect” correction. The laminar IC was significantly higher than the indirect MTL IC (z = 2.96, p = 0.0015, one-tailed; Figure 4 ), distinguishing it from the baseline connectivity in the MTL. We further explored this connection by using a classification approach, which tested whether the laminar connection was more likely to reflect a “direct” or “indirect” anatomical connection. A classifier was trained to distinguish “direct” and “indirect” MTL connections using each subject’s average IC strength as feature. This logistic ridge-regression was then applied to the laminar IC values for each subject, determining whether the laminar connection is classified as direct or indirect MTL connection. The resulting value of the regression (classifier evidence) was 0.62 (ranging from 0 [“indirect MTL”] to 1 [“direct MTL”]; p < 0.024 based on a one-tailed permutation test). Put together, these results provide empirical evidence for selective connectivity between the sEC and dEC consistent with a genuine functional connection between the layers—in keeping with the known anatomical connections between the layers ().

We next tested for the existence of the predicted functional connection between the entorhinal layers using a method known as informational connectivity (IC). The method has similarity to well-established functional connectivity analysis, but allows inference based on multivoxel information rather than global BOLD signal (cf.). This approach assesses the covariation in trial-by-trial information (decoding accuracy) between a pair of regions ( Figure 4 B). If the two regions covary positively, this indicates that the regions are functionally connected, with information passing between the two ( Figure 4 C). IC has previously been shown to accurately capture regional covariation in information content above and beyond univariate methods (), and here we applied it to assess the flow of information during inference. We measured IC between the sEC and dEC, while partialling out the information contained in all other MTL regions, ensuring that any resulting connectivity was selective to this laminar connection and was not mediated by any other MTL region. The laminar IC was significant (mean rho = 0.11 ± 0.01, z = 4.46, p < 0.0001), indicating a selective functional connection between the EC layers.

(E) Group means of informational connectivity. Error bars display the SEM. Both the selective laminar connectivity (mean rho = 0.11 ± 0.01) and direct MTL (mean rho = 0.096 ± 0.002) connectivity are larger (p < 0.001, one-tailed) than the indirect MTL connectivity baseline (mean rho = 0.065 ± 0.003), suggesting that the laminar connection may reflect a direct flow of information between the EC layers.

(D) Connectivity graph of brain regions of interest (). Connectivity patterns were used to divide all possible pairwise connections into “direct” (connections on the graph) or “indirect” (off graph) MTL connections, except the laminar EC connection, which we treat separately.

(C) Trial-by-trial covariation between the sEC and dEC information is displayed for one example subject (the subject with median connectivity strength).

(B) Informational connectivity between two regions is calculated by obtaining the decoding accuracies (scene/object category reactivation) for each trial and region for a given subject and correlating these time series pairwise (adding all other regions’ time series as covariates).

The influence of low-level stimulus features on the representation of contexts, items, and their mnemonic associations.

Having found evidence for distinct laminar signals in the EC, we next turned to our first main hypothesis. If episodic information is recirculated from the entorhinal output (deep) layers back to the input (superficial) layers during AC test trials, then category information (scene or object) about the linking B item should be present not only in the deep (dEC), but also the superficial (sEC) layer activations ( Figure 1 B). To investigate this neural “reactivation” during AC test trials, we used multivariate voxel decoding to classify the stimulus category (object or scene) of the non-presented linking item (image B). As images A and C were always faces, successful classification can only be due to reactivation of item B. All decoding analyses were based on anatomical masks specific to each subject’s anatomy ( Figures 3 and S3 ) in native space, and all reported results are based on unsmoothed, bilateral data (results for each hemisphere separately are reported in Tables S1 and S2 ). First, consistent with previous multivariate studies involving the cued recall of objects and scenes (), we found significant reactivation throughout the MTL regions ( Figure 3 C; for individual hippocampal subfields, see Figure S7 ). Crucially, however, we also detected significant reactivation within the sEC (1.7% ± 0.07%, z = 2.45, p = 0.007, one-tailed; Figure 3 D), suggesting that information may be recirculated from the dEC back into the hippocampus via the sEC. As detailed earlier, there are potential hemodynamic confounds between the cortical layers driven by the BOLD sensitivity to the cortical vasculature (). In order to ensure that the classification result in the sEC was not driven by BOLD originating solely from dEC activations, we reran the classification analysis after first removing the local dEC signal from each sEC voxel ( STAR Methods Figure S5 ). This analysis ensures that remaining signal in the sEC cannot be driven by any local spatial correlations caused by draining vein artifacts (for a conceptually similar approach, see). The classification result in the sEC was still significant after regressing out the local dEC signal (1.8% ± 0.6%, z = 2.73, p = 0.0032, one-tailed), thereby providing evidence that there is unique reactivation information present in the sEC that is unlikely to be attributed to local vasculature confounds such as draining veins ().

While our previous analysis provides evidence for reactivation in the EC as a whole, our key predictions depend on the ability to detect laminar-specific signals in the dEC and sEC layers. Detecting separate signals in different cortical layers can be challenging due to potential correlation in the BOLD signal driven by the vasculature of laminar cortex (; also see Discussion ). In order to investigate the structure of the laminar signal in our dataset, and to investigate the presence of layer-specific signals, we took a subject-specific, task-independent approach. We used an automated segmentation method ( STAR Methods Figure S6 ) to segment the EC of each subject into the superficial (input) and deep (output) layers, approximately corresponding to cortical layers II/III and IV/V, respectively (). Segmentations were based on each subject’s native-space high-resolution T2 images, therefore creating a set of subject-specific laminar segmentations ( Figures 3 and S3 ). For each voxel in both the sEC and dEC, we extracted the event-specific signal for each trial across the entire experiment (including encoding trials, AB/BC, and AC test trials), thereby forming a temporal response profile. If there is detectable layer-specific signal, it should be possible to determine whether a voxel belongs to the superficial or deep layer based on its temporal responses. To directly test this question, we trained a multivariate classifier to differentiate superficial and deep voxels based on their temporal response profiles. The results were robustly significant (mean ± SD, 85.9% ± 0.7%; sign-rank test on Z scores obtained by permutation, z = 4.46, p < 0.001), demonstrating separable signal over the course of the experiment ( Figures 3 B and S3 B). To determine whether this was still the case when specifically focusing on the set of trials of interest to our main hypothesis (AC test trials), we repeated the analysis based only on the temporal responses derived from the set of AC test trials. The classification result still remained robust (67.3% ± 0.6%; z = 4.46, p < 0.001), validating that the signal present in each layer is separable during the set of AC test trials. Importantly, this difference cannot be attributed to mean differences across layers, but can only be based on the temporal responses, as the voxel time courses were standardized prior to classification. We additionally directly compared the temporal signal and contrast to noise ratios (tSNR and CNR) across the layers (; also see STAR Methods and Figure S6 ). We found no significant difference in CNR (based on picture presentations versus fixations; mean ± SD; sEC, 0.054 ± 0.12; dEC, 0.064 ± 0.084; sign-rank test on difference, z = 0.57, p = 0.57, two-tailed), though there was a significantly stronger tSNR in the dEC compared to sEC (mean ± SD; sEC, 7.32 ± 0.73; dEC, 8.41 ± 0.92; sign-rank test on difference, z = 4.46, p < 0.0001, two-tailed). This latter is consistent with some degree of MTL signal dropout, which would influence the layers closer to the cortical surface (i.e., the superficial layers;). Importantly, however, the successful layer classification described above demonstrates that robust differences between the EC layers are still present despite this dropout. Further, it also provides additional validation of the automated laminar segmentation protocol, as separable signal could only be present if the layers have been accurately segmented.

(C) We find reactivation of memory content (scene/object distinction in AC test trials, in which no scene or object was displayed) significantly above chance throughout the MTL (EC, 2.3% ± 0.6%, p < 0.001; HC, 3.5% ± 0.5%, p < 0.001; PHC, 4.2% ± 0.7%, p < 0.001; PRC, 2.2% ± 0.5%, p < 0.001).

(B) Depiction of classification evidence on a single-subject mean EPI, classifying whether a voxel belongs to the sEC or dEC based on its activity profile over trials (red and green indicate maximal evidence for a voxel belonging to the sEC or dEC, respectively; see Figure S3 B for all subjects). The subject with the median classification accuracy across voxels is shown.

We next turned to the AC test trials ( Figure 1 ). Given that no scene or object information was visually presented on these trials, the presence of any category signal can only be driven by reactivation of memory content within the medial temporal cortex. We also found evidence for a posterior-anterior gradient coding for SvO (average r = 0.183 ± 0.082; sign-rank test against 0, z = 2.17, p = 0.015, one-tailed) during AC test trials, thereby demonstrating that the medial temporal cortex signal reflects memory reactivation at the time of inference. We ran a further analysis specifically focusing on AC test trial reactivation within the EC. In order to do so, we first selected the 100 voxels within the EC that displayed the strongest activation for scenes over objects (SvO) and vice versa (OvS) during the AB/BC test trials to use as two functional regions of interest. For each subject, we then extracted the average t value of these two sets of voxels from the SvO contrast in the AC test trials. A direct comparison revealed a significant difference in relative scene and object reactivation between these two (SvO and OvS) sets of voxels (mean difference, 3.13 ± 0.86; sign-rank test against 0, z = 2.98, p = 0.0014, one-tailed; see Figure S4 for single-subject activation maps). This shows that the same EC voxels that discriminate scenes and objects during presentation (AB/BC test trials) are also involved in memory reactivation (AC test trials). These results establish that the expected signals of reactivation of the linking (B) item are present within the EC.

In order to investigate the functional properties of the entorhinal cortical layers, we first needed to establish the presence of detectable scene and object signals in the fMRI data. We considered the whole-brain scenes versus objects (SvO) univariate contrast during AB/BC test trials ( Figure 1 ) on the group level. These trials will contain a mixture of both retrieved and visually presented information, both of which likely contribute to any resultant scene and object information. In a whole-brain analysis, we found significant SvO activation spanning the entire posterior medial temporal lobe (MTL) to the occipital lobe ( Figure 2 B), consistent with the literature on scene representation (). Further, consistent with previous studies of the medial temporal cortex (), we found evidence for a significant posterior-anterior gradient of scene-to-object signal ( STAR Methods Figure 2 C; average r = 0.81 ± 0.013; sign-rank test against 0, z = 4.46, p < 0.0001, one-tailed).

Behaviorally, subjects had the highest accuracy for AB test trials ( Figure 2 A; significant difference across all conditions, F(2,50) = 69.88, p < 0.0001; mean ± SEM; 0.89 ± 0.012; significantly larger than BC [per definition the second pair observed from each triad], t(25) = 2.41, p = 0.021 and AC, t(25) = 10.21, p < 0.0001), followed by BC (0.88 ± 0.016; significantly larger than AC, t(25) = 8.07, p < 0.0001), and lowest for AC (0.76 ± 0.019). The reaction times followed this pattern inversely with the AB test trials being the fastest (significant difference across all conditions, F(2,50) = 317.3, p < 0.0001; 2,013 ± 54; significantly faster than BC, t(25) = 6.62, p < 0.0001 and AC, t(25) = 18.66, p < 0.0001), followed by BC (2,127 ± 54.3, significantly faster than AC, t(25) = 17.88, p < 0.0001), and slowest for AC (2,935 ± 47.4). Note that while incorrect trials had significantly longer RTs (F(1,25) = 29.47, p < 0.0001, the slowed response for AC trials was also observed when only considering correct trials (significantly slower than AB, t(25) = 19.83, p < 0.0001 and BC, t(25) = 18.53, p < 0.0001). This pattern of results matches those observed in a previous study and is well accounted for by the REMERGE big-loop recurrent model ().

(C) Displaying the scene versus object group-level contrast restricted to the MTL to visualize a posterior-anterior gradient for scenes and objects, respectively; contrast scaled from negative (objects) to positive (scenes); note that the scaling is uneven to display the relatively weaker object activation.

(B) The scene versus object group-level contrast in direct test trials (AB and BC) (p < 0.001, uncorrected) spans the posterior MTL to occipital cortex, incorporating both the posterior parahippocampal gyrus and retrosplenial cortex.

(A) Behavioral results. Subjects displayed above-chance accuracy on AC trials, and show the expected pattern of results, with AC test performance significantly lower than on direct (AB/BC) test trials, and reaction time significantly higher on AC test trials. Error bars display the SEM.

We scanned 26 subjects while they performed a PAI task ( Figure 1 A). During encoding, subjects viewed pairs of images. For each pair (AB), each subject later saw another related pair (BC) that shared one image in common (B). This creates stimulus triads such that A and C are indirectly associated even though they have never been presented together. Direct test trials test whether a subject remembers the correct images being presented together (hereafter “AB/BC test trials”), thereby assessing associative memory. By contrast, indirect test trials investigate whether the subjects are able to infer the indirect association between A and C images (hereafter “AC test trials”), which depends on the integration of information across related episodes (i.e., AB, BC). The focus of this experiment was on the neural mechanisms present during these AC test trials.

Discussion

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Kumaran D. Retrieval-based model accounts for striking profile of episodic memory and generalization. We set out to provide evidence for the existence of a big-loop recurrent circuit using ultra-high-resolution fMRI and multivoxel pattern analysis, applied to the PAI task. Solving the inference trials of this task requires the integration of information across temporally distinct episodes. If such integration requires big-loop recurrence, we should see evidence of recirculation of information from the output (dEC) back to the input (sEC). A series of targeted analyses revealed a coherent set of results, each consistent with the presence of a big-loop mechanism. First, we found evidence for reactivation of the unseen “bridging: element (e.g., B item in AC test trial) required for successful inference not only in the region that receives the output of the hippocampus (dEC), but also in the layer processing the input (sEC). This reactivation is only expected if a recirculation mechanism is recruited. Second, we found that the layers of the EC displayed IC consistent with a selective connection between the two. Further, an analysis of the individual variation in layer reactivation and IC strength provided evidence for a flow of information, from the dEC back into the sEC, consistent with the operation of a big-loop pathway. Third, laminar IC predicted the flow of information from the input layer (sEC) back into the dentate gyrus and CA3, consistent with the hypothesis that big-loop recurrence allows retrieved content to requery related experiences stored in these regions. Finally, the strength of information flow through the entorhinal big loop predicted individual variation in inference performance. Put together, our findings offer the first evidence in any species of the functional importance of big-loop recurrence—involving recirculation of the output of the hippocampal system from the deep layer of the EC as a new input on the superficial layer—and establish its link to successful performance in a classic task requiring inferential behavior. Importantly, the demonstration of big-loop recurrence bolsters theoretical frameworks that propose that the hippocampus is able to support two functions that place seemingly opposing demands on the system: episodic memory and the rapid integration of information across episodes (; also see). While these accounts have received support from computational modeling of behavioral and neural data (), they have lacked the critical empirical evidence that we provide here concerning the core underlying circuit-level mechanism, namely big-loop recurrence.

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Amaral D.G. Entorhinal cortex of the rat: organization of intrinsic connections. To ensure this was true of our own data, we used a task-independent analysis to determine the presence of laminar-specific signals within our data. Specifically, a classifier trained on the set of voxel temporal response profiles was able to differentiate superficial from deep voxels ( Figures 3 B and S3 B), clearly demonstrating that the entorhinal layers contain separable BOLD signal. Further, for each analysis, we took additional steps to ensure that the results could not be attributed to vasculature-driven laminar confounds. First, we demonstrated that sEC reactivation was still present even after removing the spatially proximal dEC signal from every sEC voxel (for a conceptually similar approach, see). This ensured that the sEC signals could not be driven by hemodynamic signals originating in neighboring dEC voxels, as might occur in the case of draining vein artifacts (). Second, we applied the same local-signal correction prior to measuring the IC between the sEC and dEC, and still found evidence for a significant selective laminar connection. We also applied a second control analysis to rule out local vasculature influences on laminar connectivity, by explicitly measuring only non-local connectivity between the entorhinal layers. This was achieved by partitioning each layer into medial and lateral portions, and assessing laminar IC across different portions. Despite the fact that only non-local connectivity could be detected using this method, we still found a significant effect, consistent with the known existence of non-local anatomical connections (). Each of these control analyses removes the potential contribution of local vasculature confounds, and together they provide clear evidence for the presence of a genuine functional connection between the sEC and dEC layers. In addition to these explicit control analyses, there are also some key results that cannot be explained by vasculature confounds. First, we found that individual variation in the strength of laminar IC correlates selectively with sEC, and not dEC reactivation. If the sEC signal and laminar IC were both influenced by hemodynamic artifacts originating in the dEC, it would not be possible to find any such laminar-specific correlations, as the two layers would covary in their reactivation strength. Second, laminar IC correlated with AC performance, while dEC reactivation did not. Again, if the sEC reactivation and connectivity signals were spuriously influenced by signals from the dEC, this selective correlation with performance would not be present. Put together, these stringent control analyses and laminar-specific correlation results converge in providing strong evidence for the predicted recirculation signals that cannot be accounted for by any signals originating in the deep entorhinal layers.

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Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. We consider two further potential limitations of our data. First, when dealing with sub-millimeter resolution imaging and laminar separation, results are more prone to noise effects from imperfect motion correction or coregistration. In order to apply stringent quality control, all masks were manually inspected and corrected when necessary prior to analysis. To highlight the quality of the masks, we display single-subject-level masks for every subject in EPI space in the Supplemental Information Figure S3 ). Notably, successful differentiation of the BOLD signal depends on an accurate segmentation of the layers, as well as coregistration with the EPI data. If any of these steps resulted in substantive misalignment, the detection of layer-specific BOLD would not be possible. Further, any serious residual head motion artifacts remaining after preprocessing would also reduce our ability to detect layer-specific BOLD. As described in the previous passage, we found that a classifier could successfully differentiate the BOLD signal in the two EC layers, which provides a robust quality control check to rule out any significant impact from preprocessing error or residual motion. Second, while the absolute classification accuracies in each region are low, they are comparable with other decoding analyses within the region (e.g.,). Further, there is no straightforward relationship between classification accuracy and the underlying effect size or biological interpretation (). Indeed, the overall classification accuracy is not typically the measure of interest, as it is not itself a statistical test, and cannot be interpreted as a measure of effect size (). Rather, it is the nature of the statistical significance test performed on the classification accuracy that is key for appropriate interpretation, which in our case is at the group level. It is worth noting that, despite the low average decoding accuracy within the sEC, the variance in decoding across individuals correlated with laminar connectivity and inference, in each case with a medium effect size (i.e., between 0.3 and 0.5). This indicates that the variance across a relatively narrow band of decoding accuracies is nevertheless meaningful with respect to other neural metrics and behavior.