Functional Specialization and Coordination in the Amygdala-Hippocampus Circuit Support Working Memory

Functional Specialization and Coordination in the Amygdala-Hippocampus Circuit Support Working Memory | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Functional Specialization and Coordination in the Amygdala-Hippocampus Circuit Support Working Memory Jin Li, Dan Cao, Vasileios Dimakopoulos, Shan Yu, Xinyu Xiao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1500621/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 May, 2023 Read the published version in Nature Communications → Version 2 posted You are reading this latest preprint version Show more versions Abstract Both the hippocampus and amygdala are involved in working memory (WM) processing. However, it is still an open question how the two structures interact to represent and maintain WM content. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task. Using a series of univariate, multivariate, and connectivity analyses, our results revealed a functional specialization of the amygdala-hippocampus circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were less specific but remained stable in the absence of the stimulus. Furthermore, the amygdala and hippocampus coordinated their activity during WM: The hippocampal representation was reinstated in the amygdala during maintenance, with task induced information flow from the hippocampus to the amygdala. Importantly, the functional specificity and coordination were correlated only when they appeared in correct trials. Our study provides new evidence that successful WM processing in humans is associated with specialized and coordinated functions within the amygdala-hippocampus circuit. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Significance Human working memory (WM) processing involves the hippocampus and amygdala, as evidenced by persistent single neuron firing. However, it remains unclear what role the two regions play and how they interact during WM. By combining intracranial EEG recordings from the two regions and representational analysis, we show that the mnemonic representations during encoding in the amygdala are more specific but decrease during maintenance. Hippocampal representations are less specific but remain stable during maintenance. We also reveal coordinated representational reinstatement and information flow between the amygdala and hippocampus. These results suggest that functional specialization and coordination are fundamental to the formation and retention of WM. Introduction Working memory (WM) is fundamental for human cognition and allows storing information in an active and readily available state for a short period of time 1 . Studies have reported widely distributed brain regions, including the prefrontal, parietal, and sensory cortices, that are essential for forming and maintaining working memory content 2 . However, whether the amygdala-hippocampus circuit participates in working memory processes has remained unclear. The amygdala is classically associated with emotional processing 3 , while recent studies also showed that the amygdala has multidimensional response properties 4 and plays a role even in memorizing non-emotional stimulus material 5 . On the other hand, beyond its well-known role in forming new long-term memories 6 , the hippocampal involvement in WM is also reported by intracranial electroencephalography (iEEG) 7 , 8 and functional magnetic resonance imaging 9 studies. Single-neuron recordings in human amygdala and hippocampus found persistent firing during WM processing 10 , 11 , 12 . However, the more general significance of amygdala and hippocampal activity for dealing with WM information and their specific contribution to WM is still unknown. Multivariate analysis methods such as representational similarity analysis have been used to inform the representation of specific components (see ref. 13 for a review). Research has identified two properties related to memory performance, the representational dissimilarity between distinct pieces of information 14 and the reinstatement of memory representations 15 . The amygdala is known as a detector of goal-related stimuli 16 and receives major projections from the anterior temporal lobe 17 that convey highly processed object information 18 . On the other hand, the hippocampus is crucial for memory consolidation 19 . For instance, recent studies suggested notable overlap in representational patterns between the encoding and the post-encoding period in the hippocampus 15 , 20 . However, no study has yet simultaneously tracked amygdala and hippocampal representations in humans and addressed their interaction specifically in WM. Tract tracing studies have uncovered structural connections between the amygdala and the hippocampus 21 . This anatomical connectivity suggests a functional communication between these structures. This is supported by rodent studies 22 , 23 showing that activation by direct electrical stimulation of one region can improve synaptic plasticity in the other region and by human studies 5 indicating that stimulation of the amygdala led to increased power in the hippocampus and improved subsequent memory even during non-emotional events. Further evidence came from human iEEG studies demonstrating functional interactions between these two structures during episodic memory processing 15 , 24 . However, it remains to be established whether the engagement of the two structures is coordinated or independent. To address these issues, we recorded iEEG simultaneously from the amygdala and the hippocampus in 14 pre-surgical epilepsy patients while they performed a WM task. Here we utilized three analytical approaches: univariate activation analysis (Fig. 1 A), multivariate representational similarity analysis (RSA) (Fig. 1 (B-D) ), and connectivity analysis (Fig. 1 (E) ). We found that the amygdala contributed directly to WM by constructing highly distinct mnemonic representations during encoding while the hippocampus participated in WM by spontaneously reinstating stimulus-driven activity patterns during maintenance. Next, the information in the hippocampus during encoding was reinstated in the amygdala during maintenance, which indicates coordinated reinstatement. This was accompanied by unidirectional information flow from the hippocampus to the amygdala during encoding and maintenance. Finally, this functional specialization and coordination pattern was predictive of later memory performance. Together, these findings provide evidence for functional specialization and coordinated memory reinstatement between the amygdala and the hippocampus as a mechanism that supports WM processing. Results Task, behavior and recording channels Fourteen patients with drug resistant epilepsy (7 females) (Table 1 ) performed a modified Sternberg WM task (65 total sessions from 14 subjects) during an invasive presurgical evaluation. In this task, the items were presented simultaneously rather than sequentially, thus separating the encoding period from the maintenance period. In each trial, the subject was asked to memorize a set of 4, 6, or 8 letters presented for 2 s (encoding). After a delay (maintenance) period of 3 s, a probe letter was presented and the subject responded whether the probe letter was identical to one of the letters held in memory (retrieval) (Fig. 2 (A) ). The average accuracy was 91.9%±3.2% (range 86.1%-97.6%). The mean response time was faster for correct than incorrect trials (1.44 ± 0.36 versus 1.95 ± 0.66 seconds, paired t test: t (13) = -4.15, p = 0.0011). Hence, the subjects performed well in the task. Unless stated otherwise, we report results for correct trials only. Table 1 Subject characteristics Subject Age Gender Recording sites (amygdala) Recording sites (hippocampus) Retrieval Accuracy RT for correct trials (mean ± std) 1 24 Female 2 6 0.925 1.26 ± 0.63 2 39 Male 4 8 0.864 1.28 ± 0.65 3 18 Female 2 6 0.932 1.10 ± 0.34 4 28 Male 4 8 0.949 1.48 ± 0.63 5 20 Female 2 4 0.913 1.41 ± 0.90 6 31 Male 4 8 0.943 1.47 ± 0.59 7 47 Male 4 8 0.949 1.44 ± 0.57 8 56 Female 4 7 0.900 1.41 ± 0.51 9 19 Female 4 8 0.928 1.24 ± 0.33 10 35 Male 4 6 0.930 1.64 ± 0.73 11 51 Female 4 6 0.914 1.30 ± 0.58 12 30 Male 4 7 0.895 1.49 ± 0.77 13 29 Female 4 4 0.976 1.05 ± 0.28 14 56 Male 4 8 0.861 2.58 ± 1.22 Local field potentials (LFPs) were recorded simultaneously from depth electrodes implanted in the amygdala and hippocampus (Fig. 2 (B) ). In total from all subjects, we recorded from 94 channels in the hippocampus and 50 channels in the amygdala (see the details in Methods ). Working memory induced oscillatory response in amygdala and hippocampus We calculated the time-frequency power for each channel within the amygdala and the hippocampus, and the power outputs were z -scored against pretrial (500 ms) baseline distributions to assess the significance of the task-induced power effects per trial. Both the amygdala and the hippocampus showed sustained activity in the frequency range (1–40 Hz), especially during the encoding and the maintenance phase (Fig. 3 (A) ). We then focused on the temporally resolved changes in amplitudes of 1–40 Hz in the amygdala and the hippocampus (Fig. 3 (B) ). Both amygdala and hippocampus showed elevated oscillatory activities (1–40 Hz) relative to the baseline ( p < 0.05, cluster-based permutation test), suggesting that both regions are engaged in the working memory processing. The results in the present study match the findings reported in our previous study 25 . We next asked whether the engagement of the amygdala and the hippocampus was preferentially associated with a particular task period. We averaged the spectral power during encoding and maintenance and compared the two spectra. The z -scored power in the hippocampus during maintenance was higher than during encoding in the theta-alpha frequency band (3–13 Hz, gray shaded window in Fig. 3 (C) right , p 0.05, cluster-based permutation test, Fig. 3 (C) left ). Next, we separately extracted the theta-alpha power (3–13 Hz) during encoding and maintenance for amygdala and hippocampus and performed a repeated measure ANOVA (RMANOVA) with the extracted z -power as the dependent variable and two within-subject factors, area (amygdala/hippocampus) and task period (encoding/maintenance), and their interaction as independent variables. As shown in Fig. 3 (D) , we found a significant interaction ( p = 0.001, F (1,13) = 19.043). A simple effect analysis further indicated that the hippocampus power in the maintenance was significantly higher than in the encoding ( p = 0.0017) while no significant difference was found between encoding and maintenance in the amygdala ( p = 0.20); the power in the amygdala was higher than in the hippocampus during encoding ( p = 0.017), and the power in the hippocampus was higher than in the amygdala during maintenance but this did not reach statistical significance ( p = 0.054). Together, the univariate analyses indicate that the amygdala was preferentially engaged in encoding and the hippocampus was preferentially engaged in maintenance. Functional specialization: Representation in the amygdala is specific during encoding We next applied multivariate analysis to investigate how information is represented across multiple channels and different spectral powers at different times. We performed a series of representational similarity analyses (RSA) to investigate two representational properties crucial for memory performance, i.e., the distinctiveness and the stability of neural representations 26 . We first quantified representational dissimilarity separately for the amygdala and hippocampus. Given that both the amygdala and hippocampus showed oscillatory activities across 1–40 Hz (Fig. 3 (A) ), we used the spectral power in this frequency band as the feature for the RSA. We correlated the power from every two trials across channels and frequencies (1 to 40 Hz in steps of 1 Hz) in consecutive overlapping time windows of 100 ms (step width 10 ms Fig. 2 (C) ). Then the dissimilarity (1 - similarity) of the representational patterns was averaged across all trial-pairs, resulting in a temporal map of encoding-encoding dissimilarity (EED) across all subjects for the amygdala (Fig. 4 (A) left) and the hippocampus (Fig. 4 (A) right) separately. Next, we compared the EED map between the amygdala and the hippocampus using cluster-based permutation tests. Across all encoding time windows, a cluster with higher EED values in the amygdala than in the hippocampus appeared (outlined in black in Fig. 4 (B) , p = 0.007, cluster-based permutation); there was no cluster with higher EED values in the hippocampus than in the amygdala. Further, we averaged the EED values in significant clusters for all subjects and compared them for the amygdala and the hippocampus. This revealed higher EED values in the amygdala than in the hippocampus across subjects ( p = 0.027, t (13) = 2.49, paired t test, Fig. 4 (C) ). As the letter strings were different across trials, these findings indicate that the amygdala represented distinct WM information in a more specific neural pattern during encoding. Functional specialization: Hippocampal representation is stable from encoding to maintenance We next examined whether and how stable representational structures were maintained in the absence of stimuli in the amygdala and the hippocampus. Representational stability was indexed by memory reinstatement, an approach borrowed from the long-term memory literature 27 . Memory reinstatement was quantified as the correlation between patterns of oscillatory power across channels and frequencies (1 to 40 Hz in steps of 1 Hz) within consecutive overlapping time windows of 100 ms (step width 10 ms) for each combination of the encoding-maintenance time bins in the same trial. The correlation matrix was then averaged across trials, resulting in a temporal map of encoding-maintenance similarity (EMS) for the amygdala and the hippocampus ( Fig. 2 (D) ). The EMS values were higher in the hippocampus than in the amygdala for every encoding-maintenance time pair in the EMS map (Fig. 4 (D) ). And, the averaged EMS values in the hippocampus was higher than that in the amygdala across subjects ( p = 0.0049, t (13) = 3.38, paired t test, Fig. 4 (E) ). These findings indicated that the hippocampus retained WM information in a more stable representation during maintenance than the amygdala. Was the functional specialization of the amygdala and the hippocampus correlated? To answer this question, we carried out a correlation analysis between the EED Amy−Hipp (EED Amy - EED Hipp ) and the EMS Hipp−Amy (EMS Hipp - EMS Amy ) for each subject. We found a significantly positive correlation (Spearman’s correlation, p = 0.0067, r = 0.701, Fig. 4 (F) , which remained significant after removing an outlier ( p = 0.025)). This suggests that in subjects in which the amygdala represented more WM information, the hippocampus maintained the representation more stably. Functional coordination: Coordination of the amygdala and hippocampus in representational reinstatement In addition to functional specialization, functional coordination between brain regions is considered to be another important mechanism in WM 28 . To test whether the amygdala and hippocampus work independently or interactively in WM, we investigated whether the representational structure during encoding in one structure was reinstated in the other structure during maintenance and vice versa. We calculated cross-region EMS on each channel pair (one from the amygdala, one from the hippocampus in the same hemisphere in the same subject). The cross-region EMS maps were then averaged across channel pairs and trials, resulting in two cross-region EMS maps, one between the hippocampal encoding-amygdala maintenance combination (Fig. 5 (A), left) , the other between the amygdala encoding-hippocampal maintenance combination ( Fig. 5 (A) , right). We found substantial cross-region EMS between the amygdala and the hippocampus (Fig. 5 (A) , rho > 0). Next, we compared the two cross-region EMS maps using cluster-based permutation tests and found a significant cluster (outlined in black in Fig. 5 (B) , p = 0.015, cluster-based permutation test), showing that stronger hippocampal representational structures during encoding were reinstated by the amygdala during maintenance. The averaged EMS values within the significant clusters were higher in the hippocampal encoding-amygdala maintenance direction than in the opposite direction across subjects ( p = 0.0015, t (13) = 4.006, paired t test, Fig. 5 (C) ). These results provide evidence that the amygdala and the hippocampus coordinate their activity to maintain the representation and that the hippocampus conveys information to the amygdala. Since we found both functional specialization and coordination between the amygdala and the hippocampus in WM, we then tested for a possible correlation between the functional differentiation and collaboration. Spearman’s correlation was separately carried out between the coordinated EMS and the EED Amyg−Hipp and between the coordinated EMS and the EMS Hipp−Amyg . Intriguingly, we found a significant positive correlation between the coordinated EMS and the EMS Hipp−Amy ( p = 0.015, r = 0.644, Fig. 5 (D) ), but the positive correlation between the coordinated EMS and the EED Amyg−Hipp was not significant ( p = 0.120, r = 0.437, Fig. 5 (E) ). These findings suggest that in subjects with more information maintained in the hippocampus, the activities between the two regions were more coordinated. Functional coordination: Directional information transfer from the hippocampus to the amygdala tracks WM processing Cross-region EMS analysis found that WM representation in one region is reinstated in the other. This suggest that WM representations are distributed and coordinated across the amygdala and hippocampus. To further investigate the directionality of information transfer between these regions, we computed the spectral Granger causality (GC) index from the hippocampus to the amygdala and that in the reverse direction, during encoding and maintenance separately. The GC for both directions was significantly above the threshold (see Methods for details, grey lines denote the thresholds in Fig. 6 (A) ). Moreover, the GC from in the hippocampus to the amygdala was higher than the GC in the reverse direction in the 2–40 Hz band both during encoding (Fig. 6 (A) top , black line, p = 0.014, cluster-based permutation test) and during maintenance (Fig. 6 (A) bottom , black line, p = 0.004, cluster-based permutation test). The averaged GC values from hippocampus to amygdala were also higher than the reverse direction during encoding ( p = 0.0072, t (13) = 2.88, paired t test, Fig. 6 (B) top ) as well as during maintenance ( p = 0.0015, t (13) = 2.58, paired t test, Fig. 6 (B) bottom ) across subjects. These findings match the coordinated reinstatement and provide converging evidence of hippocampus-driven directional hippocampal-amygdala communication during WM maintenance. Notably, to exclude the volume conduction effect, we used the bipolar rereferencing scheme and obtained a highly similar pattern of hippocampus-driven information flow during encoding and maintenance ( Fig. S1 ). Functional specialization and coordination in the amygdala-hippocampal circuit support successful WM outcomes Functional specialization and coordination were found in the amygdala-hippocampal circuit during WM processing for the correct trials. However, it was still unclear whether the two properties were specific to successful WM processing. As a control, we repeated the above analyses for the incorrect trials. We calculated the z -scored power within the amygdala and the hippocampus across 3–13 Hz during encoding and maintenance for the incorrect trials and compared them using RMANOVA with two within factors: area (amygdala/hippocampus) × phase (encoding/maintenance). No interaction effect was found for the incorrect trials ( p = 0.11, Fig. 7 (A) ). We also calculated the EED map during encoding within the amygdala and the hippocampus for the incorrect trials. The EED map of the amygdala was compared with that of the hippocampus using cluster-based permutation, and no significant clusters were found (Fig. 7 (B) , p > 0.05, cluster-based permutation test). Next, we computed the EMS for the incorrect trials within the amygdala and the hippocampus separately. Contrasting the within-region EMS between the two regions revealed no difference (Fig. 7 (C) left , p = 0.086, t (13) = 1.861, paired t -test). In addition, we did not find any difference in the cross-region EMS based on the error trials (Fig. 7 (C) right , p = 0.17, t (13) = 1.47, paired t -test). This suggests that functional specification and coordination support successful WM processing. To exclude possible bias from having an imbalanced number of correct and incorrect trials, we performed control analyses by bootstrapping 10 times. Specifically, we recomputed the EMS within the amygdala and the hippocampus on a subset of trials that contained the same number of correct trials and incorrect trials. These subsets of trials were randomly selected from all the correct trials, and the process was repeated 10 times. As shown in Fig. S2(A) , Pearson’s correlation was computed and revealed high correlations between the EMS value for each subset and those from all the trials both in the amygdala ( r = 0.975 ± .013) or the hippocampus ( r = 0.976 ± .025). In each subset, the EMS within the hippocampus was higher than that within the amygdala ( Fig. S2(B) ). We thereby replicated our findings using a small subset of trials. Next, we recomputed the EMS across regions based on 10 subsets. Again, we found significant association between the EMS from each subset and those from all the trials, both in the combination of hippocampal encoding and amygdala maintenance ( r = 0.965 ± .015, Pearson’s correlation) and in the amygdala encoding and hippocampal maintenance pair ( r = 0.961 ± .014, Pearson’s correlation) (as shown in Fig. S2(C) ). Again, for each subset, the EMS between the hippocampal encoding and the amygdala maintenance was higher than the EMS in the opposite direction ( Fig. S2(D) ). Taken together, our findings ruled out bias from having an imbalanced number of correct and incorrect trials. To conclude, successful WM performance was promoted both by functional differentiation, including preferred stages of increased power, more distinct representation during encoding in the amygdala, and more stable representation reinstatement during maintenance in the hippocampus and by functional coordination, including biased cross-region reinstatement and directional information flow from the hippocampus to the amygdala. Discussion Because previous research focused mainly on the role of the neocortex in WM, ranging from the sensory to the parietal and prefrontal cortices (for a review, see 2 ), the contribution of the amygdala-hippocampal circuit in WM had only begun to be elucidated. Here we addressed the question of how the amygdala and hippocampus contribute to WM by analyzing oscillatory activity, neural representations, and inter-regional information flow. We found that 1) the amygdala was involved in memory specificity during encoding; 2) the hippocampus retained the representation of WM information in the absence of a stimulus on the screen; 3) the representational reinstatement in the amygdala and the hippocampus was coordinated; and 4) functional specificity and functional coordination within the amygdala-hippocampus circuit occurred during correct trials but was absent during incorrect trials. The amygdala has long been known to be related to emotion, thus, a role of the amygdala in memory representational specificity during encoding might appear surprising. However, a significant body of evidence has indirectly implicated a role of the amygdala in memory specificity. First, a recent human study reported that the amygdala has a higher proportion of concept cells with specific responses to preferred stimulus than the hippocampus 11 . Other studies suggested that the amygdala plays a stimulus specific role in novelty detection 29 , 30 and encodes state-dependent exploratory behavior 31 . Both functions require specific memory representations. Our finding is also consistent with recent literature highlighting a broader function for the amygdala, including processing of sensory, memory, valence, etc. 4 . Taken together, our study provided new evidence that the amygdala might contribute to WM, in particular by encoding specific memories with distinct representations. The hippocampal representations, on the other hand, were less specific but could be better retained than those in the amygdala. The lower specificity of hippocampus representation is consistent with the recent idea that the hippocampus supported less-specific representations that hold across experiences 32 . The higher EMS in the hippocampus is in line with our previous work revealing a higher proportion of maintenance cells in the hippocampus than in the amygdala 12 . Studies in long term memory also suggested that memory consolidation in the hippocampus may start early, even at the end of encoding 15 , suggesting that during this post-encoding period, memory representations are maintained in the hippocampus as memory engrams, so that they can be used to recover information later. Taken together, we infer that the hippocampus may contribute to WM maintenance by keeping representations stable in the absence of the stimulus. In addition to the above-mentioned functional specificity, we found functional coordination between the amygdala and the hippocampus during WM processing. This coordination included coordinated representational reinstatement, and inter-regional informational flow between the two structures. The interregional communication is consistent with rodent 33 and monkey 34 studies, which reported anatomical connection by using tracing techniques, and with rodent studies, which found synaptic plasticity in the amygdala induced by electrical stimulation to the hippocampus 23 , and vice versa 22 . Human iEEG studies found inter-regional functional connectivity during emotional information 35 and emotional memory 24 processing. In the current study we extended the functional coordination in the amygdala-hippocampal circuit to WM processing even in the absence of explicit emotional content. Both the coordinated representational reinstatement analysis and the GC analysis revealed a directional coordination driven by the hippocampus. This indicated a modulatory effect of the hippocampus on the amygdala in WM processing. This direction is opposite to that during emotion processing, which showed an amygdala influence on the hippocampus 35 . This raises the novel possibility that the directional information transfer from the amygdala could contribute to emotion, while that from the hippocampus could have a role in supporting WM. This inference fits with studies of fear learning in which hippocampal neurons convey contextual representations through projections to the amygdala 36 , 37 . Further evidence came from a rodent study demonstrating that a subset of hippocampal neurons, whose activity was necessary for memory encoding, was enriched in the hippocampal projection to the amygdala 38 . A recent study found that in episodic memory, mnemonic representations in the anterior hippocampus are highly distinct but decreases over time, while the posterior hippocampal representations are less distinct and do not decline over time 39 . Our result is in line with this study that in the anterior-posterior axis in amygdala and hippocampus, the representation is more specific in the anterior region and more stable overtime in the posterior region. Together, our data support a model in which the representation in the amygdala during encoding is more specific than that in the hippocampus but cannot be well retained. To at least partially solve this dilemma, the hippocampus would convey WM information to the amygdala through coordinated representational reinstatement and information transfer to help the amygdala better retain the WM information, which in turn would facilitate subsequent WM processing. There are two major theories about the brain's cognitive function, the first is the theory that modularity supports functional specialization 40 . Our results support this theory by showing that more specific representation structures occur in the amygdala during encoding and that a higher encoding-maintenance similarity in representation structure exists in the hippocampus. The second theory, distributive processing, proposes that the brain is more interactive and its regions are functionally interconnected 41 . Our findings support this theory by showing cross-regional EMS and inter-regional information flow. Interestingly, we also observed a correlation between functional coordination and functional specialization, but functional specialization and coordination only existed in correct WM trials. Thus, these two properties of the amygdala-hippocampus circuit might be essential mechanisms supporting successful WM processing. In summary, our results demonstrated functional specialization between the amygdala and hippocampus in working memory. In addition, we showed both coordinated reinstatement and information flow from the hippocampus to the amygdala. Taken together, we provide a novel and mechanistic explanation of how the oscillatory signatures of neuronal activity in the two structures especially contribute and orchestrate to support working memory. Methods Subject characteristics Data were obtained from epilepsy patients undergoing intracranial EEG monitoring at the Swiss Epilepsy Clinic, Klinik Lengg, Switzerland, to localize epileptic foci for potential surgical resection. Intracranial depth electrodes (1.3 mm diameter, 8 contacts of 1.6 mm length, 5 mm spacing; Ad-Tech, Racine, WI, www.adtechmedical.com ) were stereotactically implanted. The electrode placements were guided exclusively by clinical needs. Subjects having channels in both the amygdala and the hippocampus in the same hemisphere were included in the current study. Before testing, all the subjects provided written informed consent for the study, which had been approved by the relevant institutional ethics review board (Kantonale Ethikkommission Zürich, PB 2016–02055). In total, 14 patients (mean ± SD [range]: 34.5 ± 12.6 [18–56]; 7 females) participated in this study. There were no seizures recorded during any of the epochs, and any epochs with interictal epileptiform activity were excluded from analysis. Task We used a modified Sternberg task in which the encoding of memory content, maintenance, and recall were temporally separated (Fig. 2 (A) ). Each trial started with a fixation period (1 s) followed by the presentation of the stimulus in the encoding period (2 s). The stimulus consisted of a set of letters at the center of the screen. After the encoding period, the stimulus was replaced by a fixation square during the maintenance period (3 s). Finally, a probe letter appeared and the subjects responded with a button press (“IN” or “OUT”) to indicate whether the probe was part of the stimulus letter set. The subjects performed 50 trials per session, which lasted approximately 10 min. Some subjects performed up to seven sessions of the task. Channel localization The channels were localized using postimplantation computed tomography (CT) scans and postimplantation structural T1-weighted MRI scans. For each patient, the CT scan was co-registered to the postimplantation scan, as implemented in FieldTrip 42 . The channels were visually marked on the coregistered CT-MR images. Channel positions were verified by the neurosurgeon (L.S.) after merging pre-operative MRI with postimplantation CT images of each individual patient in the plane along the electrode (iPlan Stereotaxy 3.0, Brainlab, München, Germany). Channel locations in native space for each patient were projected to MNI space and are shown in Fig. 3 (B) . The final dataset contained 94 channels in the hippocampus and 50 channels in the amygdala across all patients. There were 6.7 ± 1.4 (range 4–8) channels per patient in the hippocampus and 3.6 ± 0.9 (range 2–4) channels per subject in the amygdala. Channel selection Each subject had 0–1 electrode targeting the anterior and posterior hippocampus and the amygdala per hemisphere. Targeted regions and hemispheres varied across subjects for clinical reasons and included the hippocampus in the left (n = 13) and right (n = 14) hemispheres and the amygdala in the left (n = 13) and right (n = 11) hemispheres. We selected the two most medial channels on each electrode targeting the hippocampus or the amygdala, as was done in previous studies 27 , 43 . This procedure was used to minimize inter-individual variability, which would be higher if different numbers of channels would have been selected across subjects. The final number of selected channels in each region for each subject is listed in Table 1 . We included only ipsilateral channel pairs in the analysis. Data acquisition and preprocessing Intracranial data were acquired against a common intracranial reference using a Neuralynx ATLAS recording system, sampled at 4 kHz, and analog-filtered above 0.5 Hz. After data acquisition, neural recordings were down sampled to 1 kHz and band-pass filtered between 1 to 200 Hz using the zero-phase delay finite impulse response (FIR) filter with Hamming window. Line noise harmonics were removed using a discrete Fourier transform. The filtered data were manually inspected to mark any channels containing epileptiform activity or artifacts for exclusion. The data were then re-referenced to the average of the signal over all the clean channels 25 . We then segmented the preprocessed data into event-related epochs; 1 s fixation period, 2 s encoding period, 3 s maintenance period, and 2 s retrieval period. We rejected trials with artifacts by visual inspection (53/3250 or 1.6% of all trials). We performed preprocessing routines with the FieldTrip 42 , EEGLAB 44 toolboxes and custom scripts in MATLAB. Time-frequency analysis Time-frequency power was separately computed for correct and incorrect trials. For each trial for each channel, we convolved the signal with complex-valued Morlet wavelets (6 cycles) to obtain power information at each frequency from 1 to 100 Hz in 1 Hz steps with a time resolution of 1 ms 20 . The task-induced power was analyzed per trial using a statistical bootstrapping procedure as was done in previous studies 25 , 45 . Briefly, for each channel and frequency, a null distribution was created by randomly selecting and averaging several data points in the baseline power (500 ms pretrial) 1,000 times, the raw power for each time point during task was then z -scored by comparing it to the null distribution to generate the z -scored power. For each subject, the z -scored spectral power was separately averaged across the encoding and maintenance periods within the amygdala and the hippocampus. Next, we compared the z -scored spectral power between encoding and maintenance across subjects separately for the amygdala and the hippocampus using a cluster-based permutation test which computes statistics at the cluster level and corrects for multiple comparisons 46 . Differences were quantified by t values. The t values corresponding to uncorrected p values of 0.05 or less were clustered. The differences were considered significant when the maximum of the cluster-level summed t values in the true data exceeded the threshold of a Monte Carlo distribution, which was created by randomly shuffling the labels of the regions 1000 times, and p < 0.05 was considered as significant. We found significantly higher z -scored spectral power during maintenance than encoding period across 3–13 Hz (theta-alpha band) in the hippocampus via a cluster-based permutation test and found no significant difference in any frequency band period in the amygdala. Further, we extracted the theta-alpha band z -scored power during encoding and maintenance within the amygdala and the hippocampus separately. We performed RMANOVA with the extracted z -power as the dependent variable and two within-subject factors and their interaction as independent variables: area (amygdala/hippocampus) and period (encoding/maintenance). A simple effect analysis was made if the interaction effect was significant ( p < 0.05). Representational dissimilarity analysis A sliding time window approach was applied to calculate the representational dissimilarity in a 100 ms sliding time window (step width 10 ms). The z -scored power was first averaged across the time points within each sliding window for each trial. The generated z -scored power of all channels and frequencies (1–40 Hz) within each time window were then vectorized. The Spearman’s correlation between the features of the two trials was calculated and Fisher z -transformed. The generated values were subtracted from 1 and then averaged across trial pairs to index the dissimilarity among trials in the given time window pair. After these steps, we got the encoding-encoding dissimilarity (EED) map across all time windows. Notably, for each subject, the number of trial pairs computed in the correct trials was the same as those in the incorrect trials, to exclude the impact of different number of trials for the correct and incorrect outcomes. The analysis procedure is presented in Fig. 2 (C) . We then compared the EED map in the amygdala with the hippocampus at the group level by using a cluster-based permutation test 46 . We also extracted the average EED values in the significant cluster in the two regions for each subject and then contrasted them via paired t tests at the group level. Representational similarity between encoding and maintenance Next, we calculated the representation similarity between encoding and maintenance (EMS) periods within the same trials for the correct and the incorrect trials separately. First, we built representational patterns based on distributed oscillatory power (1–40 Hz) across all channels for each subject between all pairs of time windows (one from encoding and the other from maintenance), resulting in EMS maps within the same trials between all encoding-maintenance time window pairs, which were then averaged across trials. Figure 2 (D) describes the detailed analysis procedure (red and blue squares). Next, we contrasted the EMS maps for the amygdala and the hippocampus by using a cluster-based permutation test. Since we found significant contrast in every encoding-maintenance time pair in the EMS map, we then extracted the average EMS values in the whole map for each subject from the two regions and contrasted them via paired t tests at the group level. To rule out the confounding effect of different numbers between the correct and incorrect trials in the EMS calculation, for each subject we selected subsets with the same number of trials from the correct trials as from the incorrect trials, and recalculated the EMS based on the extracted trials. This procedure was repeated 10 times. For each subset, EMS amygdala and EMS hippocampus were generated to see if the finding of higher EMS hippocampus value found from all correct trials could be replicated with a reduced number of trials. Coordinated reinstatement analyses between the amygdala and the hippocampus To assess whether there was a coordinated representational reinstatement between the amygdala and the hippocampus, we also computed the cross-regional representation reinstatement. The z -scored power spectral values (1–40 Hz) of one channel within each time window were vectorized. The cross-region representational reinstatement was then obtained by calculating the Spearman’s rho between the features of one channel from the amygdala during encoding and of one channel from the hippocampus in the same hemisphere during maintenance and vice versa. We then Fisher z -transformed the rho scores and averaged the coordinated reinstatement maps across all trials. For each subject, we obtained two coordinated reinstatement maps: one was the representational reinstatement between hippocampus encoding and amygdala maintenance (EMS hippenc_amymaint ) and the other was between amygdala encoding and hippocampus maintenance (EMS amyenc_hippmaint ). We also presented the coordinated reinstatement maps in the bottom of Fig. 2 (D) (purple and orange squares). Then, we compared the EMS hippenc_amymaint with the EMS amyenc_hippmaint at the group level using a cluster-based permutation test 46 . Then we extracted the values within significant clusters for each subject and contrasted them via paired t tests at the group level. Again, to avoid the confounding effect of unbalanced trial numbers between correct and incorrect trials, we performed a control analyses. Specifically, we re-performed the same analysis 10 times by randomly selecting the same number of correct trials as the incorrect trials. For each subset, we computed the average EMS hippenc_amymaint and EMS amyenc_hippmaint for each subject and then averaged these across subjects to see if the result from all correct trials could be replicated in a reduced number of trials. Granger causality analysis We examined the directionality of the amygdala-hippocampus synchronization across 1–40 Hz using spectral Granger causality (GC), which quantifies the prediction error of the signal in the frequency domain by introducing another time series. For each channel pair, the trial-wise mean was subtracted from each trial before being fit to an autoregressive model and computing the spectral GC. We then applied the Multivariate Granger Causality Matlab Toolbox 47 based on the Akaike information criterion to define the model order for each pair. The GC index was computed for both directions (from the amygdala to the hippocampus and the reverse direction) during encoding and maintenance for the correct and incorrect trials separately. Then we created a null distribution by randomly swapping the signal between channels 200 times. A GC value with a channel pair above the 95th percentile of the null distribution was considered as significant. The spectral GC values from two directions across 1–40 Hz were then averaged across all pairs for each subject. We then used a cluster-based permutation test 46 to further examine whether the spectral GC values differed between directions at frequency points at the group level. As described above, we created a Monte Carlo distribution by randomly shuffling the labels of the directions 1000 times. The differences between directions were considered significant when the maximum of the cluster-level summed t values in the true data exceeded the 95th percentile of the null distribution ( p < 0.05). We found more directional information transfer from the hippocampus to the amygdala in both encoding and maintenance. Next, we averaged the GC values of two directions across 1–40 Hz in each subject in the encoding and maintenance period separately and then compared them between the two directions at the group level via paired t tests. Declarations Data availability The dataset is freely available for download at https://doi.gin.g-node.org/10.12751/g-node.d76994/ . The task is freely available for download at http://www.neurobs.com/ex_files/expt_view?id=266 . Links to updates and further data sets can be found at https://hfozuri.ch . Code availability Standard software packages (EEGLAB, Fieldtrip) were used for processing the iEEG data in addition to custom Matlab scripts. Custom-written code is available upon reasonable request from the corresponding author (T.J.). Funding and Disclosures This work received support from the following sources: Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200200), Science Frontier Program of the Chinese Academy of Sciences (grant No. XDBS01030200), National Key R&D Program of China (grant No. 2017YFA0105203), National Natural Science Foundation of China (grant Nos. 31300934, 82151307), Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLYB2004), Open Research Fund of the CAS Key Laboratory of Behavioral Science, Institute of Psychology, and the Swiss National Science Foundation (SNSF 176222 to J.S.). Acknowledgments The authors also thank Rhoda E. Perozzi and Edmund F. Perozzi, PhDs, for English and content editing assistance. Author contributions Conceptualization, T.J., and J.S.; Methodology, J.L. and D.C.; Data Collection, V.D., L.I., and L.S.; Formal Analysis: J.L., D.C., and X.X.; Writing – Original Draft, J.L. and D.C.; Writing – Review & Editing, T.J., J.S., and S.Y.; Funding Acquisition, T.J., J.S., S.Y., and J.L.; Resources, T.J., and J.S.; Supervision, T.J., and J.S. Conflict of Interest statement The authors declare no competing financial interests. References Baddeley A. Working memory: theories, models, and controversies. Annu Rev Psychol 63 , 1–29 (2012). Christophel TB, Klink PC, Spitzer B, Roelfsema PR, Haynes JD. The Distributed Nature of Working Memory. Trends Cogn Sci 21 , 111–124 (2017). LaBar KS, Cabeza R. Cognitive neuroscience of emotional memory. Nat Rev Neurosci 7 , 54–64 (2006). Gothard KM. Multidimensional processing in the amygdala. Nat Rev Neurosci 21 , 565–575 (2020). Inman CS, et al. Direct electrical stimulation of the amygdala enhances declarative memory in humans. 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A Comparison of Auditory Oddball Responses in Dorsolateral Prefrontal Cortex, Basolateral Amygdala, and Auditory Cortex of Macaque. J Cogn Neurosci 31 , 1054–1064 (2019). Fustinana MS, Eichlisberger T, Bouwmeester T, Bitterman Y, Luthi A. State-dependent encoding of exploratory behaviour in the amygdala. Nature 592 , 267–271 (2021). Schapiro AC, Turk-Browne NB, Botvinick MM, Norman KA. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci 372 , (2017). Kishi T, Tsumori T, Yokota S, Yasui Y. Topographical projection from the hippocampal formation to the amygdala: a combined anterograde and retrograde tracing study in the rat. J Comp Neurol 496 , 349–368 (2006). Amaral DG, Cowan WM. Subcortical afferents to the hippocampal formation in the monkey. J Comp Neurol 189 , 573–591 (1980). Zheng J, et al. 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Supplementary Files Fig.S1.pdf Figure S1 Fig.S2.pdf Figure S2 Cite Share Download PDF Status: Published Journal Publication published 22 May, 2023 Read the published version in Nature Communications → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1500621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2022-03-31 20:28:40","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"articleType":"Article","associatedPublications":[],"authors":[{"id":117968928,"identity":"70b9c7db-7849-4892-a7cb-d2f5b4a56b1c","order_by":0,"name":"Jin Li","email":"","orcid":"","institution":"1 Brainnetome Center, 2 National Laboratory of Pattern Recognition","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Li","suffix":""},{"id":117968929,"identity":"d862df29-0174-4519-a7eb-6924bf93a765","order_by":1,"name":"Dan Cao","email":"","orcid":"","institution":"1 Brainnetome Center, 2 National Laboratory of Pattern Recognition","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Cao","suffix":""},{"id":117968930,"identity":"fd6f4931-d1f6-4da4-9637-aaeea88d76b8","order_by":2,"name":"Vasileios Dimakopoulos","email":"","orcid":"https://orcid.org/0000-0001-9490-565X","institution":"Department of Neurosurgery, University Hospital Zurich, University of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Vasileios","middleName":"","lastName":"Dimakopoulos","suffix":""},{"id":117968931,"identity":"1be0f1a8-0237-4954-8e82-9e25134d4239","order_by":3,"name":"Shan Yu","email":"","orcid":"","institution":"1 Brainnetome Center, 2 National Laboratory of Pattern Recognition","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Yu","suffix":""},{"id":117968932,"identity":"1450d73e-3f32-4d2a-b5c8-a8c472e531de","order_by":4,"name":"Xinyu Xiao","email":"","orcid":"","institution":"School of Artificial Intelligence, University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Xiao","suffix":""},{"id":117968933,"identity":"a83a86a3-dca0-4ef9-aa9d-ffb824bcc2df","order_by":5,"name":"Lukas Imbach","email":"","orcid":"","institution":"Klinik Lengg AG","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Imbach","suffix":""},{"id":117968934,"identity":"637e2f45-7b0f-4311-b4f8-c62ea01ded79","order_by":6,"name":"Lennart Stieglitz","email":"","orcid":"https://orcid.org/0000-0002-3744-5105","institution":"University Hospital of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Lennart","middleName":"","lastName":"Stieglitz","suffix":""},{"id":117968935,"identity":"d67be5e9-eac2-418c-a6b3-08157211230f","order_by":7,"name":"Johannes Sarnthein","email":"","orcid":"https://orcid.org/0000-0001-9141-381X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Sarnthein","suffix":""},{"id":117968936,"identity":"f8e64887-c041-4f26-a3d3-21ac17ffc3e2","order_by":8,"name":"Tianzi Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3RoQrCQBzH8d84mOXUOplsr3BjoEV8lr8MNAkmi6CDBZNYfQyTYlMOXPEBrBbzQJQFgxvTJuei4b7hrtwH/ncH6HR/mBEVu4NKvonShOCDlSVFhF7Iyh5mc3a93NPpYMuqxwSjLurzvXEbKQcz236TzOEuqgUriADWiZi9UhK0bIv4cC25n90lG+8Mk3ElqTwyYg1EQWZwfxPeaiQk6E0kRAkyttEnby1rAUjE3Dv1IltFvGW8aaSdqSvihUTynDhOLA83JQmzhy4O8Px/8tUIFQBw8+HSD9HpdDrdt15XITvgippYbwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9531-291X","institution":"Brainnetome Center, Institute of Automation, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Tianzi","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2022-03-29 08:30:48","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1500621/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1500621/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-023-38571-w","type":"published","date":"2023-05-22T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":23459917,"identity":"8623ab46-33fc-4404-b920-e2ff192e2d81","added_by":"auto","created_at":"2022-07-05 16:03:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":667795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy framework.\u003c/strong\u003e To investigate the function of the amygdala and hippocampus in WM processing, we performed a series of analyses. (A) First, we extracted and compared the task-induced power in the amygdala and the hippocampus during encoding and maintenance. Next, we calculated (B) the encoding-encoding representational dissimilarity between trials, (C) the within-region encoding-maintenance similarity (EMS), and (D) the cross-region EMS. (E) Finally, we examined the directionality of the information flow through the amygdala-hippocampal circuit. All analyses were performed separately for correct and incorrect trials.\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.32.09AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/c9725816997874e3fdc7d96d.png"},{"id":23459920,"identity":"004a25c3-59c9-46b2-b6fc-1b70c5317fc4","added_by":"auto","created_at":"2022-07-05 16:03:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":601615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorking memory task, recording sites, and representational similarity analysis.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) In each trial a set of consonants was presented (encoding 2 s) followed by a delay (maintenance 3 s). After the delay, a probe letter was shown and the subjects indicated whether the probe was in the initial set of consonants (retrieval).\u003c/p\u003e\u003cp\u003e(B) Channel location across subjects in MNI152 space (see Methods). Recording regions are indicated by different colors (red, amygdala; blue, hippocampus).\u003c/p\u003e\u003cp\u003e(C) Encoding-encoding dissimilarity (EED) analysis. EED was performed by correlating power across 40 frequency bins (between 1 and 40 Hz) and across all channels within the amygdala or the hippocampus in consecutive time windows of 100 ms (step width 10 ms), across various time windows in the encoding period (top and middle). We correlated the activity patterns between two different trials, utilizing encoding-encoding time pairs (wi-wj pair). (Bottom) Resulting dissimilarity map (1-simmilarity) across various time windows during the encoding period. Warmer color denotes higher dissimilarity and cooler color means higher similarity.\u003c/p\u003e\u003cp\u003e(D) EMS analysis. Time-frequency patterns were extracted across 1-40 Hz in consecutive time windows of 100 ms (step width 10 ms). For each brain region, Spearman’s correlations were carried out between the activity patterns across channels of encoding-maintenance time pairs (wi-wj pair) in the same trial. Blue squares denote the hippocampal EMS while red ones correspond to the amygdala’s EMS. Similarly, we also computed cross-region EMS, which was similar to the within-region EMS except that in the correlation analysis the representational pattern from the encoding and maintenance periods came from channels in different brain regions (one in the amygdala and one in the ipsilateral hippocampus). Purple squares denote the EMS between the representational pattern in the amygdala’s encoding and the hippocampal maintenance period, and orange squares denote the EMS between the hippocampal encoding-amygdala’s maintenance pair.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.32.19AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/96e095660423bedfe6e14808.png"},{"id":23460213,"identity":"b9814128-e393-493b-8acd-c324432a673f","added_by":"auto","created_at":"2022-07-05 16:08:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":755789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorking memory induced oscillatory response in amygdala and hippocampus\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Task-induced power in the amygdala (Amy, left column) and the hippocampus (Hipp, right column) for correct trials. Warmer colors denote a task-induced increase in power with respect to the baseline (500 ms pretrial) and cooler colors denote a decrease. Both the amygdala and the hippocampus showed sustained task-increased power in the frequency range 1-40 Hz, especially during the encoding and maintenance periods.\u003c/p\u003e\u003cp\u003e(B) Power amplitudes (1–40 Hz), averaged across subjects (± s.e.m. shown as shading around the mean trace) and locked to stimulus onset, are shown for electrodes located in the amygdala (red) and hippocampus (blue).\u003c/p\u003e\u003cp\u003e(C) Spectral power averaged across the encoding (solid line) and the maintenance (dotted line) period in the amygdala and the hippocampus across subjects (±SEM shaded area). In the hippocampus, the spectral power during the maintenance period was significantly higher than during the encoding period (\u003cem\u003ep \u003c/em\u003e\u0026lt; .05, cluster-based permutation test) across the frequency band 3-13 Hz (gray shaded rectangle). No significant difference between periods was found in the amygdala.\u003c/p\u003e\u003cp\u003e(D) Averaged power (3-13 Hz) during encoding and maintenance in the amygdala and the hippocampus. We found a significant interaction effect between Area (amygdala/hippocampus) × Period (encoding/maintenance) (RMANOVA, \u003cem\u003ep \u003c/em\u003e= 0.001, \u003cem\u003eF\u003c/em\u003e(1,13) = 19.043). Dots denote individual subjects. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.32.29AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/a890f6a57406a2eab8b261b6.png"},{"id":23460208,"identity":"6f6d915c-2453-43ca-a998-ad33eadf176c","added_by":"auto","created_at":"2022-07-05 16:08:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":881060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentational distinctiveness and stability within the amygdala and the hippocampus.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Grand average encoding-encoding dissimilarity (EED) map for correct trials during the encoding period across all subjects, in the amygdala (left column) and the hippocampus (right column).\u003c/p\u003e\u003cp\u003e(B) EED Difference map obtained by subtracting the hippocampus EED from the amygdala EED reveals a significant cluster (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, cluster-based permutation test, outlined in black), indicating that the amygdala represents the working memory information specifically during the encoding period (dark red area, higher EED in the amygdala than the hippocampus). White areas indicate that there was no significant difference (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05) between the hippocampus and amygdala.\u003c/p\u003e\u003cp\u003e(C) EED value averaged over the significant cluster in (B) was significantly higher in the amygdala than in the hippocampus across the group of subjects (paired \u003cem\u003et\u003c/em\u003e test: \u003cem\u003ep \u003c/em\u003e= 0.027, \u003cem\u003et\u003c/em\u003e(13) = 2.49).\u003c/p\u003e\u003cp\u003e(D) Grand average EMS map across all subjects in the amygdala and the hippocampus.\u003c/p\u003e\u003cp\u003e(E) The average EMS was higher in the hippocampus than in the amygdala (paired \u003cem\u003et\u003c/em\u003e test: \u003cem\u003ep \u003c/em\u003e=0.0049, \u003cem\u003et\u003c/em\u003e(13) = 3.381).\u003c/p\u003e\u003cp\u003e(F) The functional specificity of the amygdala (EED\u003csub\u003eamygdala\u003c/sub\u003e - EED\u003csub\u003ehippocampus\u003c/sub\u003e) correlated positively with the functional specificity of the hippocampus (EMS\u003csub\u003ehippocampus\u003c/sub\u003e - EMS\u003csub\u003eamygdala\u003c/sub\u003e) (Spearman’s correlation: \u003cem\u003ep \u003c/em\u003e=0.0067, \u003cem\u003er \u003c/em\u003e=0.701). Dots indicate individual subjects.\u0026nbsp;* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.32.39AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/910f0200b1c41159c2bb0ed2.png"},{"id":23460515,"identity":"1bd99814-b93d-4f2c-8c87-d396a09e6eef","added_by":"auto","created_at":"2022-07-05 16:13:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":700990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoordinated representational reinstatement in the amygdala and the hippocampus.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Coordinated EMS between the amygdala and the hippocampus averaged across subjects in correct trials. Cross-region EMS were calculated between the hippocampal encoding and the amygdala’s maintenance representation (EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e, left) as well as between the amygdala’s encoding and the hippocampal maintenance (EMS\u003csub\u003eamyenc_hippmaint\u003c/sub\u003e, right), separately.\u003c/p\u003e\u003cp\u003e(B) Cross-region EMS difference map from two directions reveals a significant cluster (\u003cem\u003ep \u003c/em\u003e\u0026lt;0.05, cluster-based permutation test, outlined in black) with a higher value in the hippocampal encoding-amygdala’s maintenance pair (dark red area). In white areas there was no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; .05) between the two directions.\u003c/p\u003e\u003cp\u003e(C) The average EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e value (orange) in the cluster in (B) was significantly higher than EMS\u003csub\u003eamyenc_hippmaint\u003c/sub\u003e (purple) (paired \u003cem\u003et\u003c/em\u003e test: \u003cem\u003ep \u003c/em\u003e=0.0015, \u003cem\u003et\u003c/em\u003e(13) = 4.006).\u003c/p\u003e\u003cp\u003e(D) The EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e correlated positively with the functional specificity of the hippocampus (EMS\u003csub\u003ehippocampus\u003c/sub\u003e - EMS\u003csub\u003eamygdala\u003c/sub\u003e) (Spearman’s correlation: \u003cem\u003ep\u003c/em\u003e =0.015, \u003cem\u003er \u003c/em\u003e=0.644). Dots indicate individual subjects.\u003c/p\u003e\u003cp\u003e(E) The positive correlation between EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e and the functional specificity of the amygdala (EED\u003csub\u003eamygdala\u003c/sub\u003e - EED\u003csub\u003ehippocampus\u003c/sub\u003e) did not reach statistical significance (Spearman’s correlation: \u003cem\u003ep \u003c/em\u003e=0.120, \u003cem\u003er \u003c/em\u003e=0.437).\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.32.47AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/4cc98427d40dc0ab0772e8fe.png"},{"id":23460517,"identity":"28165b7f-0b9a-4806-aef5-48ca5fe3e84d","added_by":"auto","created_at":"2022-07-05 16:13:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cspan class=\"ql-cursor\"\u003e\u003c/span\u003e\u003cstrong\u003eDirectional information flow between the amygdala and the hippocampus.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Average GC index from the hippocampus to the amygdala (blue) and from the reverse direction (red) across all subjects during the encoding (left) and the maintenance (right) periods. GC from the hippocampus was stronger than that in the opposite direction during the encoding (black bar, \u003cem\u003ep \u003c/em\u003e=0.014, cluster-based permutation test) and maintenance periods (black bar, \u003cem\u003ep\u003c/em\u003e =0.004, cluster-based permutation test). Shaded areas, SEM.\u003c/p\u003e\u003cp\u003e(B) The averaged GC values from hippocampus to amygdala were also higher than the reverse direction during encoding (top, paired \u003cem\u003et\u003c/em\u003e test: \u003cem\u003ep \u003c/em\u003e=0.0072, \u003cem\u003et\u003c/em\u003e(13) = 2.88) as well as during maintenance (bottom, paired \u003cem\u003et\u003c/em\u003e test: \u003cem\u003ep \u003c/em\u003e=0.0015, \u003cem\u003et\u003c/em\u003e(13) = 2.58) across subjects. Dots denote individual subjects. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.32.55AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/d1f40f7e97712ca94e1116f9.png"},{"id":23459912,"identity":"64ed12d9-0f3d-4735-8d63-63e67f6cc865","added_by":"auto","created_at":"2022-07-05 16:03:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":634111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePower and RSA analyses in incorrect trials.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) \u003cem\u003eZ\u003c/em\u003e-scored powers for the 3-13 Hz range were averaged across subjects for the incorrect trials. There was no significant Area × Period interaction effect (RMANOVA, \u003cem\u003ep \u003c/em\u003e\u0026gt;0.05). \u003cem\u003en.s.\u003c/em\u003e means \u003cem\u003ep \u003c/em\u003e\u0026gt;0.05.\u003c/p\u003e\u003cp\u003e(B) Grand average EED map across subjects for incorrect trials in the amygdala and the hippocampus. No significant cluster was found after contrasting the two maps (\u003cem\u003ep \u003c/em\u003e\u0026gt;0.05, cluster-based permutation test).\u003c/p\u003e\u003cp\u003e(C) (Left) We found no significant difference between the EMS value in the amygdala and in the hippocampus for the incorrect trials (\u003cem\u003ep \u003c/em\u003e\u0026gt;0.05, paired \u003cem\u003et\u003c/em\u003e test). (Right)We found no significant difference between the EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e (orange) and the EMS\u003csub\u003eamyenc_hippmaint\u003c/sub\u003e pair (purple) across subjects (\u003cem\u003ep \u003c/em\u003e\u0026gt;0.05, paired \u003cem\u003et\u003c/em\u003e test). Dots denote individual subjects. \u003cem\u003en.s.\u003c/em\u003e not significant.\u003c/p\u003e","description":"","filename":"ScreenShot20220705at10.33.02AM.png","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/01ea5a1fc7724cf66c0606dc.png"},{"id":37363109,"identity":"7ee8e077-1bdb-498c-b852-071cb8b496c6","added_by":"auto","created_at":"2023-05-23 07:12:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4362081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/5f173f3f-fdc8-42af-bc7e-56e9dcae7ec6.pdf"},{"id":23461074,"identity":"02d14df1-a28d-4260-bfe2-6cd9af5887d2","added_by":"auto","created_at":"2022-07-05 16:18:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44154,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1\u003c/p\u003e","description":"","filename":"Fig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/c1de9ec0366f8111c5fdf2b6.pdf"},{"id":23460210,"identity":"4cd1a152-df4f-4e9c-8d52-cf7a7b0ee6e7","added_by":"auto","created_at":"2022-07-05 16:08:28","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":87363,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2\u003c/p\u003e","description":"","filename":"Fig.S2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1500621/v2/40a01f316765b35d93a5d5f8.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Functional Specialization and Coordination in the Amygdala-Hippocampus Circuit Support Working Memory","fulltext":[{"header":"Significance","content":"\u003cp\u003eHuman working memory (WM) processing involves the hippocampus and amygdala, as evidenced by persistent single neuron firing. However, it remains unclear what role the two regions play and how they interact during WM. By combining intracranial EEG recordings from the two regions and representational analysis, we show that the mnemonic representations during encoding in the amygdala are more specific but decrease during maintenance. Hippocampal representations are less specific but remain stable during maintenance. We also reveal coordinated representational reinstatement and information flow between the amygdala and hippocampus. These results suggest that functional specialization and coordination are fundamental to the formation and retention of WM.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWorking memory (WM) is fundamental for human cognition and allows storing information in an active and readily available state for a short period of time \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Studies have reported widely distributed brain regions, including the prefrontal, parietal, and sensory cortices, that are essential for forming and maintaining working memory content \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, whether the amygdala-hippocampus circuit participates in working memory processes has remained unclear. The amygdala is classically associated with emotional processing \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, while recent studies also showed that the amygdala has multidimensional response properties \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and plays a role even in memorizing non-emotional stimulus material \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. On the other hand, beyond its well-known role in forming new long-term memories \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, the hippocampal involvement in WM is also reported by intracranial electroencephalography (iEEG) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and functional magnetic resonance imaging \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e studies. Single-neuron recordings in human amygdala and hippocampus found persistent firing during WM processing \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, the more general significance of amygdala and hippocampal activity for dealing with WM information and their specific contribution to WM is still unknown.\u003c/p\u003e \u003cp\u003eMultivariate analysis methods such as representational similarity analysis have been used to inform the representation of specific components (see ref. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e for a review). Research has identified two properties related to memory performance, the representational dissimilarity between distinct pieces of information \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and the reinstatement of memory representations \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The amygdala is known as a detector of goal-related stimuli \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and receives major projections from the anterior temporal lobe \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e that convey highly processed object information \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. On the other hand, the hippocampus is crucial for memory consolidation \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. For instance, recent studies suggested notable overlap in representational patterns between the encoding and the post-encoding period in the hippocampus \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, no study has yet simultaneously tracked amygdala and hippocampal representations in humans and addressed their interaction specifically in WM.\u003c/p\u003e \u003cp\u003eTract tracing studies have uncovered structural connections between the amygdala and the hippocampus \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This anatomical connectivity suggests a functional communication between these structures. This is supported by rodent studies \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e showing that activation by direct electrical stimulation of one region can improve synaptic plasticity in the other region and by human studies \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e indicating that stimulation of the amygdala led to increased power in the hippocampus and improved subsequent memory even during non-emotional events. Further evidence came from human iEEG studies demonstrating functional interactions between these two structures during episodic memory processing \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, it remains to be established whether the engagement of the two structures is coordinated or independent.\u003c/p\u003e \u003cp\u003eTo address these issues, we recorded iEEG simultaneously from the amygdala and the hippocampus in 14 pre-surgical epilepsy patients while they performed a WM task. Here we utilized three analytical approaches: univariate activation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), multivariate representational similarity analysis (RSA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(B-D)\u003c/b\u003e), and connectivity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(E)\u003c/b\u003e). We found that the amygdala contributed directly to WM by constructing highly distinct mnemonic representations during encoding while the hippocampus participated in WM by spontaneously reinstating stimulus-driven activity patterns during maintenance. Next, the information in the hippocampus during encoding was reinstated in the amygdala during maintenance, which indicates coordinated reinstatement. This was accompanied by unidirectional information flow from the hippocampus to the amygdala during encoding and maintenance. Finally, this functional specialization and coordination pattern was predictive of later memory performance. Together, these findings provide evidence for functional specialization and coordinated memory reinstatement between the amygdala and the hippocampus as a mechanism that supports WM processing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTask, behavior and recording channels\u003c/h2\u003e \u003cp\u003eFourteen patients with drug resistant epilepsy (7 females) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) performed a modified Sternberg WM task (65 total sessions from 14 subjects) during an invasive presurgical evaluation. In this task, the items were presented simultaneously rather than sequentially, thus separating the encoding period from the maintenance period. In each trial, the subject was asked to memorize a set of 4, 6, or 8 letters presented for 2 s (encoding). After a delay (maintenance) period of 3 s, a probe letter was presented and the subject responded whether the probe letter was identical to one of the letters held in memory (retrieval) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e). The average accuracy was 91.9%\u0026plusmn;3.2% (range 86.1%-97.6%). The mean response time was faster for correct than incorrect trials (1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 versus 1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66 seconds, paired \u003cem\u003et\u003c/em\u003e test: \u003cem\u003et\u003c/em\u003e (13) = -4.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011). Hence, the subjects performed well in the task. Unless stated otherwise, we report results for correct trials only.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubject characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecording sites (amygdala)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecording sites (hippocampus)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRetrieval Accuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRT for correct trials (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;std)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLocal field potentials (LFPs) were recorded simultaneously from depth electrodes implanted in the amygdala and hippocampus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e). In total from all subjects, we recorded from 94 channels in the hippocampus and 50 channels in the amygdala (see the details in \u003cb\u003eMethods\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWorking memory induced oscillatory response in amygdala and hippocampus\u003c/h2\u003e \u003cp\u003eWe calculated the time-frequency power for each channel within the amygdala and the hippocampus, and the power outputs were \u003cem\u003ez\u003c/em\u003e-scored against pretrial (500 ms) baseline distributions to assess the significance of the task-induced power effects per trial. Both the amygdala and the hippocampus showed sustained activity in the frequency range (1\u0026ndash;40 Hz), especially during the encoding and the maintenance phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e). We then focused on the temporally resolved changes in amplitudes of 1\u0026ndash;40 Hz in the amygdala and the hippocampus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e). Both amygdala and hippocampus showed elevated oscillatory activities (1\u0026ndash;40 Hz) relative to the baseline (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, cluster-based permutation test), suggesting that both regions are engaged in the working memory processing. The results in the present study match the findings reported in our previous study \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next asked whether the engagement of the amygdala and the hippocampus was preferentially associated with a particular task period. We averaged the spectral power during encoding and maintenance and compared the two spectra. The \u003cem\u003ez\u003c/em\u003e-scored power in the hippocampus during maintenance was higher than during encoding in the theta-alpha frequency band (3\u0026ndash;13 Hz, gray shaded window in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(C) right\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, cluster-based permutation test). No significant difference between task periods was found in the amygdala (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, cluster-based permutation test, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(C) left\u003c/b\u003e). Next, we separately extracted the theta-alpha power (3\u0026ndash;13 Hz) during encoding and maintenance for amygdala and hippocampus and performed a repeated measure ANOVA (RMANOVA) with the extracted \u003cem\u003ez\u003c/em\u003e-power as the dependent variable and two within-subject factors, area (amygdala/hippocampus) and task period (encoding/maintenance), and their interaction as independent variables. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e, we found a significant interaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eF\u003c/em\u003e (1,13)\u0026thinsp;=\u0026thinsp;19.043). A simple effect analysis further indicated that the hippocampus power in the maintenance was significantly higher than in the encoding (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017) while no significant difference was found between encoding and maintenance in the amygdala (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20); the power in the amygdala was higher than in the hippocampus during encoding (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), and the power in the hippocampus was higher than in the amygdala during maintenance but this did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054). Together, the univariate analyses indicate that the amygdala was preferentially engaged in encoding and the hippocampus was preferentially engaged in maintenance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional specialization: Representation in the amygdala is specific during encoding\u003c/h2\u003e \u003cp\u003eWe next applied multivariate analysis to investigate how information is represented across multiple channels and different spectral powers at different times. We performed a series of representational similarity analyses (RSA) to investigate two representational properties crucial for memory performance, i.e., the distinctiveness and the stability of neural representations \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe first quantified representational dissimilarity separately for the amygdala and hippocampus. Given that both the amygdala and hippocampus showed oscillatory activities across 1\u0026ndash;40 Hz (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e), we used the spectral power in this frequency band as the feature for the RSA. We correlated the power from every two trials across channels and frequencies (1 to 40 Hz in steps of 1 Hz) in consecutive overlapping time windows of 100 ms (step width 10 ms Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(C)\u003c/b\u003e). Then the dissimilarity (1 - similarity) of the representational patterns was averaged across all trial-pairs, resulting in a temporal map of encoding-encoding dissimilarity (EED) across all subjects for the amygdala (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e left) and the hippocampus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e right) separately. Next, we compared the EED map between the amygdala and the hippocampus using cluster-based permutation tests. Across all encoding time windows, a cluster with higher EED values in the amygdala than in the hippocampus appeared (outlined in black in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, cluster-based permutation); there was no cluster with higher EED values in the hippocampus than in the amygdala. Further, we averaged the EED values in significant clusters for all subjects and compared them for the amygdala and the hippocampus. This revealed higher EED values in the amygdala than in the hippocampus across subjects (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;2.49, paired \u003cem\u003et\u003c/em\u003e test, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(C)\u003c/b\u003e). As the letter strings were different across trials, these findings indicate that the amygdala represented distinct WM information in a more specific neural pattern during encoding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional specialization: Hippocampal representation is stable from encoding to maintenance\u003c/h2\u003e \u003cp\u003eWe next examined whether and how stable representational structures were maintained in the absence of stimuli in the amygdala and the hippocampus. Representational stability was indexed by memory reinstatement, an approach borrowed from the long-term memory literature \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Memory reinstatement was quantified as the correlation between patterns of oscillatory power across channels and frequencies (1 to 40 Hz in steps of 1 Hz) within consecutive overlapping time windows of 100 ms (step width 10 ms) for each combination of the encoding-maintenance time bins in the same trial. The correlation matrix was then averaged across trials, resulting in a temporal map of encoding-maintenance similarity (EMS) for the amygdala and the hippocampus \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e). The EMS values were higher in the hippocampus than in the amygdala for every encoding-maintenance time pair in the EMS map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e). And, the averaged EMS values in the hippocampus was higher than that in the amygdala across subjects (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0049, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;3.38, paired \u003cem\u003et\u003c/em\u003e test, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(E)\u003c/b\u003e). These findings indicated that the hippocampus retained WM information in a more stable representation during maintenance than the amygdala.\u003c/p\u003e \u003cp\u003eWas the functional specialization of the amygdala and the hippocampus correlated? To answer this question, we carried out a correlation analysis between the EED\u003csub\u003eAmy\u0026minus;Hipp\u003c/sub\u003e (EED\u003csub\u003eAmy\u003c/sub\u003e - EED\u003csub\u003eHipp\u003c/sub\u003e) and the EMS\u003csub\u003eHipp\u0026minus;Amy\u003c/sub\u003e (EMS\u003csub\u003eHipp\u003c/sub\u003e - EMS\u003csub\u003eAmy\u003c/sub\u003e) for each subject. We found a significantly positive correlation (Spearman\u0026rsquo;s correlation, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0067, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.701, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(F)\u003c/b\u003e, which remained significant after removing an outlier (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025)). This suggests that in subjects in which the amygdala represented more WM information, the hippocampus maintained the representation more stably.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional coordination: Coordination of the amygdala and hippocampus in representational reinstatement\u003c/h2\u003e \u003cp\u003eIn addition to functional specialization, functional coordination between brain regions is considered to be another important mechanism in WM \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. To test whether the amygdala and hippocampus work independently or interactively in WM, we investigated whether the representational structure during encoding in one structure was reinstated in the other structure during maintenance and vice versa.\u003c/p\u003e \u003cp\u003eWe calculated cross-region EMS on each channel pair (one from the amygdala, one from the hippocampus in the same hemisphere in the same subject). The cross-region EMS maps were then averaged across channel pairs and trials, resulting in two cross-region EMS maps, one between the hippocampal encoding-amygdala maintenance combination (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(A), left)\u003c/b\u003e, the other between the amygdala encoding-hippocampal maintenance combination \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e, right). We found substantial cross-region EMS between the amygdala and the hippocampus (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e, rho\u0026thinsp;\u0026gt;\u0026thinsp;0). Next, we compared the two cross-region EMS maps using cluster-based permutation tests and found a significant cluster (outlined in black in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, cluster-based permutation test), showing that stronger hippocampal representational structures during encoding were reinstated by the amygdala during maintenance. The averaged EMS values within the significant clusters were higher in the hippocampal encoding-amygdala maintenance direction than in the opposite direction across subjects (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;4.006, paired \u003cem\u003et\u003c/em\u003e test, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(C)\u003c/b\u003e). These results provide evidence that the amygdala and the hippocampus coordinate their activity to maintain the representation and that the hippocampus conveys information to the amygdala.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince we found both functional specialization and coordination between the amygdala and the hippocampus in WM, we then tested for a possible correlation between the functional differentiation and collaboration. Spearman\u0026rsquo;s correlation was separately carried out between the coordinated EMS and the EED\u003csub\u003eAmyg\u0026minus;Hipp\u003c/sub\u003e and between the coordinated EMS and the EMS\u003csub\u003eHipp\u0026minus;Amyg\u003c/sub\u003e. Intriguingly, we found a significant positive correlation between the coordinated EMS and the EMS\u003csub\u003eHipp\u0026minus;Amy\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.644, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e), but the positive correlation between the coordinated EMS and the EED\u003csub\u003eAmyg\u0026minus;Hipp\u003c/sub\u003e was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.120, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.437, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(E)\u003c/b\u003e). These findings suggest that in subjects with more information maintained in the hippocampus, the activities between the two regions were more coordinated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional coordination: Directional information transfer from the hippocampus to the amygdala tracks WM processing\u003c/h2\u003e \u003cp\u003eCross-region EMS analysis found that WM representation in one region is reinstated in the other. This suggest that WM representations are distributed and coordinated across the amygdala and hippocampus. To further investigate the directionality of information transfer between these regions, we computed the spectral Granger causality (GC) index from the hippocampus to the amygdala and that in the reverse direction, during encoding and maintenance separately. The GC for both directions was significantly above the threshold (see \u003cb\u003eMethods\u003c/b\u003e for details, grey lines denote the thresholds in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e). Moreover, the GC from in the hippocampus to the amygdala was higher than the GC in the reverse direction in the 2\u0026ndash;40 Hz band both during encoding (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(A) top\u003c/b\u003e, black line, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, cluster-based permutation test) and during maintenance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(A) bottom\u003c/b\u003e, black line, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, cluster-based permutation test). The averaged GC values from hippocampus to amygdala were also higher than the reverse direction during encoding (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0072, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;2.88, paired \u003cem\u003et\u003c/em\u003e test, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(B) top\u003c/b\u003e) as well as during maintenance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;2.58, paired \u003cem\u003et\u003c/em\u003e test, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(B) bottom\u003c/b\u003e) across subjects. These findings match the coordinated reinstatement and provide converging evidence of hippocampus-driven directional hippocampal-amygdala communication during WM maintenance. Notably, to exclude the volume conduction effect, we used the bipolar rereferencing scheme and obtained a highly similar pattern of hippocampus-driven information flow during encoding and maintenance (\u003cb\u003eFig. S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFunctional specialization and coordination in the amygdala-hippocampal circuit support successful WM outcomes\u003c/h2\u003e \u003cp\u003eFunctional specialization and coordination were found in the amygdala-hippocampal circuit during WM processing for the correct trials. However, it was still unclear whether the two properties were specific to successful WM processing. As a control, we repeated the above analyses for the incorrect trials.\u003c/p\u003e \u003cp\u003eWe calculated the \u003cem\u003ez\u003c/em\u003e-scored power within the amygdala and the hippocampus across 3\u0026ndash;13 Hz during encoding and maintenance for the incorrect trials and compared them using RMANOVA with two within factors: area (amygdala/hippocampus) \u0026times; phase (encoding/maintenance). No interaction effect was found for the incorrect trials (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e). We also calculated the EED map during encoding within the amygdala and the hippocampus for the incorrect trials. The EED map of the amygdala was compared with that of the hippocampus using cluster-based permutation, and no significant clusters were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, cluster-based permutation test). Next, we computed the EMS for the incorrect trials within the amygdala and the hippocampus separately. Contrasting the within-region EMS between the two regions revealed no difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e(C) left\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.086, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;1.861, paired \u003cem\u003et\u003c/em\u003e-test). In addition, we did not find any difference in the cross-region EMS based on the error trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e(C) right\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003et\u003c/em\u003e(13)\u0026thinsp;=\u0026thinsp;1.47, paired \u003cem\u003et\u003c/em\u003e-test). This suggests that functional specification and coordination support successful WM processing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo exclude possible bias from having an imbalanced number of correct and incorrect trials, we performed control analyses by bootstrapping 10 times. Specifically, we recomputed the EMS within the amygdala and the hippocampus on a subset of trials that contained the same number of correct trials and incorrect trials. These subsets of trials were randomly selected from all the correct trials, and the process was repeated 10 times. As shown in \u003cb\u003eFig. S2(A)\u003c/b\u003e, Pearson\u0026rsquo;s correlation was computed and revealed high correlations between the EMS value for each subset and those from all the trials both in the amygdala (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.975\u0026thinsp;\u0026plusmn;\u0026thinsp;.013) or the hippocampus (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.976\u0026thinsp;\u0026plusmn;\u0026thinsp;.025). In each subset, the EMS within the hippocampus was higher than that within the amygdala (\u003cb\u003eFig. S2(B)\u003c/b\u003e). We thereby replicated our findings using a small subset of trials. Next, we recomputed the EMS across regions based on 10 subsets. Again, we found significant association between the EMS from each subset and those from all the trials, both in the combination of hippocampal encoding and amygdala maintenance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.965\u0026thinsp;\u0026plusmn;\u0026thinsp;.015, Pearson\u0026rsquo;s correlation) and in the amygdala encoding and hippocampal maintenance pair (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.961\u0026thinsp;\u0026plusmn;\u0026thinsp;.014, Pearson\u0026rsquo;s correlation) (as shown in \u003cb\u003eFig. S2(C)\u003c/b\u003e). Again, for each subset, the EMS between the hippocampal encoding and the amygdala maintenance was higher than the EMS in the opposite direction (\u003cb\u003eFig. S2(D)\u003c/b\u003e). Taken together, our findings ruled out bias from having an imbalanced number of correct and incorrect trials.\u003c/p\u003e \u003cp\u003eTo conclude, successful WM performance was promoted both by functional differentiation, including preferred stages of increased power, more distinct representation during encoding in the amygdala, and more stable representation reinstatement during maintenance in the hippocampus and by functional coordination, including biased cross-region reinstatement and directional information flow from the hippocampus to the amygdala.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBecause previous research focused mainly on the role of the neocortex in WM, ranging from the sensory to the parietal and prefrontal cortices (for a review, see \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), the contribution of the amygdala-hippocampal circuit in WM had only begun to be elucidated. Here we addressed the question of how the amygdala and hippocampus contribute to WM by analyzing oscillatory activity, neural representations, and inter-regional information flow. We found that 1) the amygdala was involved in memory specificity during encoding; 2) the hippocampus retained the representation of WM information in the absence of a stimulus on the screen; 3) the representational reinstatement in the amygdala and the hippocampus was coordinated; and 4) functional specificity and functional coordination within the amygdala-hippocampus circuit occurred during correct trials but was absent during incorrect trials.\u003c/p\u003e \u003cp\u003eThe amygdala has long been known to be related to emotion, thus, a role of the amygdala in memory representational specificity during encoding might appear surprising. However, a significant body of evidence has indirectly implicated a role of the amygdala in memory specificity. First, a recent human study reported that the amygdala has a higher proportion of concept cells with specific responses to preferred stimulus than the hippocampus \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Other studies suggested that the amygdala plays a stimulus specific role in novelty detection \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and encodes state-dependent exploratory behavior \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Both functions require specific memory representations. Our finding is also consistent with recent literature highlighting a broader function for the amygdala, including processing of sensory, memory, valence, etc. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Taken together, our study provided new evidence that the amygdala might contribute to WM, in particular by encoding specific memories with distinct representations.\u003c/p\u003e \u003cp\u003eThe hippocampal representations, on the other hand, were less specific but could be better retained than those in the amygdala. The lower specificity of hippocampus representation is consistent with the recent idea that the hippocampus supported less-specific representations that hold across experiences \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The higher EMS in the hippocampus is in line with our previous work revealing a higher proportion of maintenance cells in the hippocampus than in the amygdala \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Studies in long term memory also suggested that memory consolidation in the hippocampus may start early, even at the end of encoding \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, suggesting that during this post-encoding period, memory representations are maintained in the hippocampus as memory engrams, so that they can be used to recover information later. Taken together, we infer that the hippocampus may contribute to WM maintenance by keeping representations stable in the absence of the stimulus.\u003c/p\u003e \u003cp\u003eIn addition to the above-mentioned functional specificity, we found functional coordination between the amygdala and the hippocampus during WM processing. This coordination included coordinated representational reinstatement, and inter-regional informational flow between the two structures. The interregional communication is consistent with rodent \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and monkey \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e studies, which reported anatomical connection by using tracing techniques, and with rodent studies, which found synaptic plasticity in the amygdala induced by electrical stimulation to the hippocampus \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and vice versa \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Human iEEG studies found inter-regional functional connectivity during emotional information \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and emotional memory \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e processing. In the current study we extended the functional coordination in the amygdala-hippocampal circuit to WM processing even in the absence of explicit emotional content.\u003c/p\u003e \u003cp\u003eBoth the coordinated representational reinstatement analysis and the GC analysis revealed a directional coordination driven by the hippocampus. This indicated a modulatory effect of the hippocampus on the amygdala in WM processing. This direction is opposite to that during emotion processing, which showed an amygdala influence on the hippocampus \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This raises the novel possibility that the directional information transfer from the amygdala could contribute to emotion, while that from the hippocampus could have a role in supporting WM. This inference fits with studies of fear learning in which hippocampal neurons convey contextual representations through projections to the amygdala \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Further evidence came from a rodent study demonstrating that a subset of hippocampal neurons, whose activity was necessary for memory encoding, was enriched in the hippocampal projection to the amygdala \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. A recent study found that in episodic memory, mnemonic representations in the anterior hippocampus are highly distinct but decreases over time, while the posterior hippocampal representations are less distinct and do not decline over time \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our result is in line with this study that in the anterior-posterior axis in amygdala and hippocampus, the representation is more specific in the anterior region and more stable overtime in the posterior region. Together, our data support a model in which the representation in the amygdala during encoding is more specific than that in the hippocampus but cannot be well retained. To at least partially solve this dilemma, the hippocampus would convey WM information to the amygdala through coordinated representational reinstatement and information transfer to help the amygdala better retain the WM information, which in turn would facilitate subsequent WM processing.\u003c/p\u003e \u003cp\u003eThere are two major theories about the brain's cognitive function, the first is the theory that modularity supports functional specialization \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our results support this theory by showing that more specific representation structures occur in the amygdala during encoding and that a higher encoding-maintenance similarity in representation structure exists in the hippocampus. The second theory, distributive processing, proposes that the brain is more interactive and its regions are functionally interconnected \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our findings support this theory by showing cross-regional EMS and inter-regional information flow. Interestingly, we also observed a correlation between functional coordination and functional specialization, but functional specialization and coordination only existed in correct WM trials. Thus, these two properties of the amygdala-hippocampus circuit might be essential mechanisms supporting successful WM processing.\u003c/p\u003e \u003cp\u003eIn summary, our results demonstrated functional specialization between the amygdala and hippocampus in working memory. In addition, we showed both coordinated reinstatement and information flow from the hippocampus to the amygdala. Taken together, we provide a novel and mechanistic explanation of how the oscillatory signatures of neuronal activity in the two structures especially contribute and orchestrate to support working memory.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubject characteristics\u003c/h2\u003e \u003cp\u003eData were obtained from epilepsy patients undergoing intracranial EEG monitoring at the Swiss Epilepsy Clinic, Klinik Lengg, Switzerland, to localize epileptic foci for potential surgical resection. Intracranial depth electrodes (1.3 mm diameter, 8 contacts of 1.6 mm length, 5 mm spacing; Ad-Tech, Racine, WI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.adtechmedical.com\" target=\"_blank\"\u003ewww.adtechmedical.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.adtechmedical.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e were stereotactically implanted. The electrode placements were guided exclusively by clinical needs. Subjects having channels in both the amygdala and the hippocampus in the same hemisphere were included in the current study. Before testing, all the subjects provided written informed consent for the study, which had been approved by the relevant institutional ethics review board (Kantonale Ethikkommission Z\u0026uuml;rich, PB 2016\u0026ndash;02055). In total, 14 patients (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD [range]: 34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 [18\u0026ndash;56]; 7 females) participated in this study. There were no seizures recorded during any of the epochs, and any epochs with interictal epileptiform activity were excluded from analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTask\u003c/h2\u003e \u003cp\u003eWe used a modified Sternberg task in which the encoding of memory content, maintenance, and recall were temporally separated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e). Each trial started with a fixation period (1 s) followed by the presentation of the stimulus in the encoding period (2 s). The stimulus consisted of a set of letters at the center of the screen. After the encoding period, the stimulus was replaced by a fixation square during the maintenance period (3 s). Finally, a probe letter appeared and the subjects responded with a button press (\u0026ldquo;IN\u0026rdquo; or \u0026ldquo;OUT\u0026rdquo;) to indicate whether the probe was part of the stimulus letter set. The subjects performed 50 trials per session, which lasted approximately 10 min. Some subjects performed up to seven sessions of the task.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eChannel localization\u003c/h2\u003e \u003cp\u003eThe channels were localized using postimplantation computed tomography (CT) scans and postimplantation structural T1-weighted MRI scans. For each patient, the CT scan was co-registered to the postimplantation scan, as implemented in FieldTrip \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The channels were visually marked on the coregistered CT-MR images. Channel positions were verified by the neurosurgeon (L.S.) after merging pre-operative MRI with postimplantation CT images of each individual patient in the plane along the electrode (iPlan Stereotaxy 3.0, Brainlab, M\u0026uuml;nchen, Germany). Channel locations in native space for each patient were projected to MNI space and are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e. The final dataset contained 94 channels in the hippocampus and 50 channels in the amygdala across all patients. There were 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (range 4\u0026ndash;8) channels per patient in the hippocampus and 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (range 2\u0026ndash;4) channels per subject in the amygdala.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eChannel selection\u003c/h2\u003e \u003cp\u003eEach subject had 0\u0026ndash;1 electrode targeting the anterior and posterior hippocampus and the amygdala per hemisphere. Targeted regions and hemispheres varied across subjects for clinical reasons and included the hippocampus in the left (n\u0026thinsp;=\u0026thinsp;13) and right (n\u0026thinsp;=\u0026thinsp;14) hemispheres and the amygdala in the left (n\u0026thinsp;=\u0026thinsp;13) and right (n\u0026thinsp;=\u0026thinsp;11) hemispheres. We selected the two most medial channels on each electrode targeting the hippocampus or the amygdala, as was done in previous studies \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This procedure was used to minimize inter-individual variability, which would be higher if different numbers of channels would have been selected across subjects. The final number of selected channels in each region for each subject is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We included only ipsilateral channel pairs in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eIntracranial data were acquired against a common intracranial reference using a Neuralynx ATLAS recording system, sampled at 4 kHz, and analog-filtered above 0.5 Hz. After data acquisition, neural recordings were down sampled to 1 kHz and band-pass filtered between 1 to 200 Hz using the zero-phase delay finite impulse response (FIR) filter with Hamming window. Line noise harmonics were removed using a discrete Fourier transform. The filtered data were manually inspected to mark any channels containing epileptiform activity or artifacts for exclusion. The data were then re-referenced to the average of the signal over all the clean channels \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We then segmented the preprocessed data into event-related epochs; 1 s fixation period, 2 s encoding period, 3 s maintenance period, and 2 s retrieval period. We rejected trials with artifacts by visual inspection (53/3250 or 1.6% of all trials). We performed preprocessing routines with the FieldTrip \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, EEGLAB \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e toolboxes and custom scripts in MATLAB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTime-frequency analysis\u003c/h2\u003e \u003cp\u003eTime-frequency power was separately computed for correct and incorrect trials. For each trial for each channel, we convolved the signal with complex-valued Morlet wavelets (6 cycles) to obtain power information at each frequency from 1 to 100 Hz in 1 Hz steps with a time resolution of 1 ms \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The task-induced power was analyzed per trial using a statistical bootstrapping procedure as was done in previous studies \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Briefly, for each channel and frequency, a null distribution was created by randomly selecting and averaging several data points in the baseline power (500 ms pretrial) 1,000 times, the raw power for each time point during task was then \u003cem\u003ez\u003c/em\u003e-scored by comparing it to the null distribution to generate the \u003cem\u003ez\u003c/em\u003e-scored power.\u003c/p\u003e \u003cp\u003eFor each subject, the \u003cem\u003ez\u003c/em\u003e-scored spectral power was separately averaged across the encoding and maintenance periods within the amygdala and the hippocampus. Next, we compared the \u003cem\u003ez\u003c/em\u003e-scored spectral power between encoding and maintenance across subjects separately for the amygdala and the hippocampus using a cluster-based permutation test which computes statistics at the cluster level and corrects for multiple comparisons \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Differences were quantified by \u003cem\u003et\u003c/em\u003e values. The \u003cem\u003et\u003c/em\u003e values corresponding to uncorrected \u003cem\u003ep\u003c/em\u003e values of 0.05 or less were clustered. The differences were considered significant when the maximum of the cluster-level summed \u003cem\u003et\u003c/em\u003e values in the true data exceeded the threshold of a Monte Carlo distribution, which was created by randomly shuffling the labels of the regions 1000 times, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant.\u003c/p\u003e \u003cp\u003eWe found significantly higher \u003cem\u003ez\u003c/em\u003e-scored spectral power during maintenance than encoding period across 3\u0026ndash;13 Hz (theta-alpha band) in the hippocampus via a cluster-based permutation test and found no significant difference in any frequency band period in the amygdala. Further, we extracted the theta-alpha band \u003cem\u003ez\u003c/em\u003e-scored power during encoding and maintenance within the amygdala and the hippocampus separately. We performed RMANOVA with the extracted \u003cem\u003ez\u003c/em\u003e-power as the dependent variable and two within-subject factors and their interaction as independent variables: area (amygdala/hippocampus) and period (encoding/maintenance). A simple effect analysis was made if the interaction effect was significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRepresentational dissimilarity analysis\u003c/h2\u003e \u003cp\u003eA sliding time window approach was applied to calculate the representational dissimilarity in a 100 ms sliding time window (step width 10 ms). The \u003cem\u003ez\u003c/em\u003e-scored power was first averaged across the time points within each sliding window for each trial. The generated \u003cem\u003ez\u003c/em\u003e-scored power of all channels and frequencies (1\u0026ndash;40 Hz) within each time window were then vectorized. The Spearman\u0026rsquo;s correlation between the features of the two trials was calculated and Fisher \u003cem\u003ez\u003c/em\u003e-transformed. The generated values were subtracted from 1 and then averaged across trial pairs to index the dissimilarity among trials in the given time window pair. After these steps, we got the encoding-encoding dissimilarity (EED) map across all time windows. Notably, for each subject, the number of trial pairs computed in the correct trials was the same as those in the incorrect trials, to exclude the impact of different number of trials for the correct and incorrect outcomes. The analysis procedure is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(C)\u003c/b\u003e. We then compared the EED map in the amygdala with the hippocampus at the group level by using a cluster-based permutation test \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We also extracted the average EED values in the significant cluster in the two regions for each subject and then contrasted them via paired \u003cem\u003et\u003c/em\u003e tests at the group level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRepresentational similarity between encoding and maintenance\u003c/h2\u003e \u003cp\u003eNext, we calculated the representation similarity between encoding and maintenance (EMS) periods within the same trials for the correct and the incorrect trials separately. First, we built representational patterns based on distributed oscillatory power (1\u0026ndash;40 Hz) across all channels for each subject between all pairs of time windows (one from encoding and the other from maintenance), resulting in EMS maps within the same trials between all encoding-maintenance time window pairs, which were then averaged across trials. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e describes the detailed analysis procedure (red and blue squares). Next, we contrasted the EMS maps for the amygdala and the hippocampus by using a cluster-based permutation test. Since we found significant contrast in every encoding-maintenance time pair in the EMS map, we then extracted the average EMS values in the whole map for each subject from the two regions and contrasted them via paired \u003cem\u003et\u003c/em\u003e tests at the group level.\u003c/p\u003e \u003cp\u003eTo rule out the confounding effect of different numbers between the correct and incorrect trials in the EMS calculation, for each subject we selected subsets with the same number of trials from the correct trials as from the incorrect trials, and recalculated the EMS based on the extracted trials. This procedure was repeated 10 times. For each subset, EMS\u003csub\u003eamygdala\u003c/sub\u003e and EMS\u003csub\u003ehippocampus\u003c/sub\u003e were generated to see if the finding of higher EMS\u003csub\u003ehippocampus\u003c/sub\u003e value found from all correct trials could be replicated with a reduced number of trials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCoordinated reinstatement analyses between the amygdala and the hippocampus\u003c/h2\u003e \u003cp\u003eTo assess whether there was a coordinated representational reinstatement between the amygdala and the hippocampus, we also computed the cross-regional representation reinstatement. The \u003cem\u003ez\u003c/em\u003e-scored power spectral values (1\u0026ndash;40 Hz) of one channel within each time window were vectorized. The cross-region representational reinstatement was then obtained by calculating the Spearman\u0026rsquo;s rho between the features of one channel from the amygdala during encoding and of one channel from the hippocampus in the same hemisphere during maintenance and vice versa. We then Fisher \u003cem\u003ez\u003c/em\u003e-transformed the rho scores and averaged the coordinated reinstatement maps across all trials. For each subject, we obtained two coordinated reinstatement maps: one was the representational reinstatement between hippocampus encoding and amygdala maintenance (EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e) and the other was between amygdala encoding and hippocampus maintenance (EMS\u003csub\u003eamyenc_hippmaint\u003c/sub\u003e). We also presented the coordinated reinstatement maps in the bottom of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e (purple and orange squares). Then, we compared the EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e with the EMS\u003csub\u003eamyenc_hippmaint\u003c/sub\u003e at the group level using a cluster-based permutation test \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Then we extracted the values within significant clusters for each subject and contrasted them via paired \u003cem\u003et\u003c/em\u003e tests at the group level.\u003c/p\u003e \u003cp\u003eAgain, to avoid the confounding effect of unbalanced trial numbers between correct and incorrect trials, we performed a control analyses. Specifically, we re-performed the same analysis 10 times by randomly selecting the same number of correct trials as the incorrect trials. For each subset, we computed the average EMS\u003csub\u003ehippenc_amymaint\u003c/sub\u003e and EMS\u003csub\u003eamyenc_hippmaint\u003c/sub\u003e for each subject and then averaged these across subjects to see if the result from all correct trials could be replicated in a reduced number of trials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGranger causality analysis\u003c/h2\u003e \u003cp\u003eWe examined the directionality of the amygdala-hippocampus synchronization across 1\u0026ndash;40 Hz using spectral Granger causality (GC), which quantifies the prediction error of the signal in the frequency domain by introducing another time series. For each channel pair, the trial-wise mean was subtracted from each trial before being fit to an autoregressive model and computing the spectral GC. We then applied the Multivariate Granger Causality Matlab Toolbox \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e based on the Akaike information criterion to define the model order for each pair. The GC index was computed for both directions (from the amygdala to the hippocampus and the reverse direction) during encoding and maintenance for the correct and incorrect trials separately. Then we created a null distribution by randomly swapping the signal between channels 200 times. A GC value with a channel pair above the 95th percentile of the null distribution was considered as significant. The spectral GC values from two directions across 1\u0026ndash;40 Hz were then averaged across all pairs for each subject.\u003c/p\u003e \u003cp\u003eWe then used a cluster-based permutation test \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e to further examine whether the spectral GC values differed between directions at frequency points at the group level. As described above, we created a Monte Carlo distribution by randomly shuffling the labels of the directions 1000 times. The differences between directions were considered significant when the maximum of the cluster-level summed \u003cem\u003et\u003c/em\u003e values in the true data exceeded the 95th percentile of the null distribution (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We found more directional information transfer from the hippocampus to the amygdala in both encoding and maintenance. Next, we averaged the GC values of two directions across 1\u0026ndash;40 Hz in each subject in the encoding and maintenance period separately and then compared them between the two directions at the group level via paired \u003cem\u003et\u003c/em\u003e tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe dataset is freely available for download at \u003ca href=\"https://doi.gin.g-node.org/10.12751/g-node.d76994/\"\u003ehttps://doi.gin.g-node.org/10.12751/g-node.d76994/\u003c/a\u003e. The task is freely available for download at \u003ca href=\"http://www.neurobs.com/ex_files/expt_view?id=266\"\u003ehttp://www.neurobs.com/ex_files/expt_view?id=266\u003c/a\u003e. Links to updates and further data sets can be found at \u003ca href=\"https://hfozuri.ch\"\u003ehttps://hfozuri.ch\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eStandard software packages (EEGLAB, Fieldtrip) were used for processing the iEEG data in addition to custom Matlab scripts. Custom-written code is available upon reasonable request from the corresponding author (T.J.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding and Disclosures\u003c/p\u003e\n\u003cp\u003eThis work received support from the following sources: Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200200), Science Frontier Program of the Chinese Academy of Sciences (grant No. XDBS01030200), \u0026nbsp;National Key R\u0026amp;D Program of China (grant No. 2017YFA0105203), National Natural Science Foundation of China (grant Nos. 31300934, 82151307), Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLYB2004), Open Research Fund of the CAS Key Laboratory of Behavioral Science, Institute of Psychology, and the Swiss National Science Foundation (SNSF 176222 to J.S.).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors also thank Rhoda E. Perozzi and Edmund F. Perozzi, PhDs, for English and content editing assistance.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization, T.J., and J.S.; Methodology, J.L. and D.C.; Data Collection, V.D., L.I., and L.S.; Formal Analysis: J.L., D.C., and X.X.; Writing \u0026ndash; Original Draft, J.L. and D.C.; Writing \u0026ndash; Review \u0026amp; Editing, T.J., J.S., and S.Y.; Funding Acquisition, T.J., J.S., S.Y., and J.L.; Resources, T.J., and J.S.; Supervision, T.J., and J.S.\u003c/p\u003e\n\u003cp\u003eConflict of Interest statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaddeley A. Working memory: theories, models, and controversies. 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J Neurosci Methods \u003cb\u003e223\u003c/b\u003e, 50\u0026ndash;68 (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1500621/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1500621/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBoth the hippocampus and amygdala are involved in working memory (WM) processing. However, it is still an open question how the two structures interact to represent and maintain WM content. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task. Using a series of univariate, multivariate, and connectivity analyses, our results revealed a functional specialization of the amygdala-hippocampus circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were less specific but remained stable in the absence of the stimulus. Furthermore, the amygdala and hippocampus coordinated their activity during WM: The hippocampal representation was reinstated in the amygdala during maintenance, with task induced information flow from the hippocampus to the amygdala. Importantly, the functional specificity and coordination were correlated only when they appeared in correct trials. Our study provides new evidence that successful WM processing in humans is associated with specialized and coordinated functions within the amygdala-hippocampus circuit.\u003c/p\u003e","manuscriptTitle":"Functional Specialization and Coordination in the Amygdala-Hippocampus Circuit Support Working Memory","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-07-05 16:03:26","doi":"10.21203/rs.3.rs-1500621/v2","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e11ca081-f8b5-4587-9119-ce2327d5d6ae","owner":[],"postedDate":"July 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-05-23T07:11:54+00:00","versionOfRecord":{"articleIdentity":"rs-1500621","link":"https://doi.org/10.1038/s41467-023-38571-w","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2023-05-22 04:00:00","publishedOnDateReadable":"May 22nd, 2023"},"versionCreatedAt":"2022-07-05 16:03:26","video":"","vorDoi":"10.1038/s41467-023-38571-w","vorDoiUrl":"https://doi.org/10.1038/s41467-023-38571-w","workflowStages":[]},"version":"v2","identity":"rs-1500621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1500621","identity":"rs-1500621","version":["v2"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}