Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization

  1. Yannan Zhu
  2. Yimeng Zeng
  3. Jingyuan Ren
  4. Lingke Zhang
  5. Changming Chen
  6. Guillen Fernandez
  7. Shaozheng Qin  Is a corresponding author
  1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China
  2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, China
  3. Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Netherlands
  4. School of Education, Chongqing Normal University, China
  5. Chinese Institute for Brain Research, China

Abstract

Neutral events preceding emotional experiences can be better remembered, likely by assigning them as significant to guide possible use in future. Yet, the neurobiological mechanisms of how emotional learning enhances memory for past mundane events remain unclear. By two behavioral studies and one functional magnetic resonance imaging study with an adapted sensory preconditioning paradigm, we show rapid neural reactivation and connectivity changes underlying emotion-charged retroactive memory enhancement. Behaviorally, emotional learning retroactively enhanced initial memory for neutral associations across the three studies. Neurally, emotional learning potentiated trial-specific reactivation of overlapping neural traces in the hippocampus and stimulus-relevant neocortex. It further induced rapid hippocampal-neocortical functional reorganization supporting such retroactive memory benefit, as characterized by enhanced hippocampal-neocortical coupling modulated by the amygdala during emotional learning, and a shift of hippocampal connectivity from stimulus-relevant neocortex to distributed transmodal prefrontal-parietal areas at post-learning rests. Together, emotional learning retroactively promotes memory integration for past neutral events through stimulating trial-specific reactivation of overlapping representations and reorganization of associated memories into an integrated network to foster its priority for future use.

Editor's evaluation

This manuscript presents valuable insights into neural mechanisms driving emotional memory enhancements for previously neutral information. The authors present compelling behavioral evidence that memory for items indirectly paired with an aversive event is enhanced, and that these enhancements are associated with increased fMRI signal interactions between the amygdala, hippocampus, and stimulus-relevant cortex. The work will be interesting to researchers interested in learning and memory.

https://doi.org/10.7554/eLife.60190.sa0

Introduction

Emotion shapes learning and memory for our episodic experiences. Experiencing an emotional event such as a psychological trauma, for instance, often not only strengthens our memory for the event itself, but also can benefit memories for other mundane events occurring separately in time (LaBar and Cabeza, 2006; Li et al., 2008; Wong et al., 2019). There has been substantial progress in understanding the mechanisms underlying memory enhancement for emotional events themselves, owing to autonomic reactions to emotional arousal that stimulates the encoding and post-encoding processes of emotional memory (Hamann, 2001; LaBar and Cabeza, 2006). Yet, our understanding of the mechanisms of how emotional learning prompts memory-related brain systems in a way that retroactively enhances memory for past neutral events is still in its infancy. In many circumstances, the significance of our experiences such as reward or punishment often occurs after the event. Since we cannot determine which event will become significant later, human episodic memory is theorized to organize our experiences into a highly adaptive network of integrated representations, that can be prioritized in terms of the significance of preceding or following events (Ritchey et al., 2016; Shohamy and Daw, 2015). Such a mechanism allows seemingly mundane events to take on significance following a salient experience or emotional arousal, thereby generalizes their memories to future use in ever-changing environment (Holmes et al., 2018; Wong et al., 2019). However, this retroactive effect may also lead to maladaptive generalization recognized as a cognitive hallmark of core symptoms in some mental disorders, like posttraumatic stress disorder (PTSD) and phobia (Lange et al., 2019; Mary et al., 2020). Deciphering the neurobiological mechanisms of emotion-charged retroactive memory enhancement in humans is thus critical for further understanding of maladaptive generalization in these disorders.

In past decades, the emotion-charged retroactive memory effects are investigated by behavioral tagging and sensory preconditioning protocols in animals and humans. Studies of the behavioral tagging have provided prominent evidence for the delayed retroactive memory enhancement following a strongly salient or emotional experience, typically resulting in a generalized form of enhancement on initial weak memories encoded closely in time (Ballarini et al., 2009; Dunsmoor et al., 2022; Takeuchi et al., 2016). In our everyday memory, however, emotion-induced retroactive effect often occurs on some specific events through rapid trial-specific form of emotional learning. Such rapid effect reflects highly adaptative and flexible features of human episodic memory systems (Howard et al., 2015), which cannot readily be explained by the conventional behavioral tagging models. We thus focused on sensory preconditioning protocols with trial-specific associative learning tasks (Brogden, 1939). This paradigm typically consists of two stages: two neutral events (i.e. direct and indirect events) are paired together in an initial learning phase, and then the direct event is paired with a salient stimulus (i.e. threat or reward)—namely an emotional or reward learning phase (Kurth-Nelson et al., 2015; Sharpe et al., 2017; Wong et al., 2019). Animal work has shown that the salient stimulus can spread its value to the indirect neutral event through an integration mechanism (Holmes et al., 2018; Sadacca et al., 2018; Sharpe et al., 2017). Human studies have also suggested that a subsequent salient event could enhance perceptual discrimination and guide decision bias for its related neutral events (Kuhl et al., 2010; Li et al., 2008; Wimmer and Shohamy, 2012). These studies in the context of sensory preconditioning provide a probability that a retroactive memory benefit could emerge for specific neutral information that occurs before emotional learning. However, it remains elusive about the neurocognitive mechanisms of how emotional learning retroactively enhances memory for previously neutral events in a trial-specific manner.

Recent memory integration views of sensory preconditioning paradigm posit that, as new emotional experiences are encoded, related memories for past sensory (neutral) events can be reactivated and integrated with the emotional memory content, essentially resulting in a retroactive memory benefit (Holmes et al., 2022; Schlichting and Frankland, 2017; Shohamy and Daw, 2015; Wong et al., 2019). By this view, there appears an integrative encoding mechanism – that is, memories for related events are encoded into an integrated network across associations with overlapping representations (Schlichting and Preston, 2014; Shohamy and Wagner, 2008). Human neuroimaging studies provide compelling evidence supporting the integrative encoding mechanism, by which a newly encoded event can be updated and reorganized into relevant episodic memory through reactivation of overlapping neural ensembles engaged in both initial and new learning (Schlichting and Preston, 2015; Shohamy and Wagner, 2008; van Kesteren et al., 2016). The hippocampus serves as an integrative hub by binding disparate representations in stimulus-sensitive neocortical areas into episodic memories (Kuhl et al., 2010; van Kesteren et al., 2016). Reactivation of hippocampal and stimulus-sensitive neocortical representations has been well proposed to promote systems-level memory integration (Schlichting and Preston, 2014; Sutherland and McNaughton, 2000; Zhuang et al., 2022). The reactivation of overlapping neural traces is considered as a scaffold for integrating newly learnt information into existing memories, making it possible to reorganize memories according to their future significance. However, it remains unknown how such reactivation occurs at a trial-specific level during emotional learning and then contributes to memory enhancement for initially mundane information. Based on above neurobiological models and empirical observations in neuroimaging studies, we hypothesized that this emotion-charged retroactive benefit would result from increased trial-specific reactivation of hippocampal and stimulus-sensitive neocortical representations, which enhances the memory association between initially learnt events and promotes their integration.

Emotion-charged memory enhancement is most likely based on autonomic reactions associated with emotional arousal, accompanying with (nor)adrenergic signaling that modulates neural ensembles in the amygdala as well as the hippocampus and related neural circuits (Hamann, 2001; LaBar and Cabeza, 2006). Many studies have suggested that emotional arousal can lead to better episodic memory through strengthening hippocampal connectivity with the amygdala and related neocortical regions during emotional memory encoding (Dolcos et al., 2004; Hermans et al., 2014; Richardson et al., 2004; Ritchey et al., 2013). Beyond online encoding, offline processes at awake rest and sleep also contribute to emotional memory benefit, most likely involving reconfiguration of hippocampal functional connectivity with distributed neocortical networks (de Voogd et al., 2016; Schlichting and Frankland, 2017; Tambini et al., 2010). Recent studies have demonstrated that neural engagement, for instance, excitable hippocampal-neocortical coordinated interactions prior to encoding could affect the allocation of new information into specific neural populations and form a preparatory network for igniting reactivation or replay cascades to support memory integration (Josselyn and Frankland, 2018; Kaefer et al., 2022; Schlichting and Frankland, 2017; van Dongen et al., 2011). Thus, it is possible that an excitable brain state at pre-emotional-encoding rest might modulate subsequent emotional memory integration and predict memory performance. Moreover, notable emotion-modulated changes in hippocampal-neocortical connectivity at post-emotional-encoding rest are also thought to associate with subsequent memory performance (de Voogd et al., 2016; Hermans et al., 2017; Murty et al., 2017). However, it remains unknown how emotion-related online and offline mechanisms support the retroactive benefit on episodic memory. Given these previous findings, we hypothesized that emotional learning would retroactively promote memory integration for neutral events, by acting on not only hippocampal dialogue with the amygdala and related neocortical regions during online encoding, but also hippocampal-neocortical reorganization during offline resting.

Here, we tested above hypotheses by two behavioral studies and one event-related functional magnetic resonance imaging (fMRI) study using an adapted sensory preconditioning paradigm. In each of the three studies, the experimental design consisted of three phases: an initial learning, a following emotional learning and a surprise associative memory test (Figure 1A). In Study 1 (N=30), participants learnt 72 neutral face-object pairs during the initial learning phase. Thereafter, each face from the initial learning phase was presented as a cue and paired with either an aversive screaming voice (i.e. aversive condition) or a neutral voice (i.e. neutral condition) during the emotional learning phase. A surprise associative memory test was administered 30 min later to assess memory performance for initial face-object associations. In Study 2 (N=28), the reproducibility of Study 1 was examined by another independent research group. In Study 3 (N=28), participants underwent fMRI scanning during the initial and emotional learning phases that were interleaved by three rest scans, with concurrent skin conductance recording to monitor autonomic arousal. A set of multi-voxel pattern similarity (Zhuang et al., 2022), task-dependent functional connectivity and machine learning-based prediction analyses, in conjunction with exploratory task-free functional connectivity and mediation analyses, were implemented to assess trial-specific reactivation of initial learning activity, functional coupling during online emotional learning and offline post-initial/emotional-learning rests, as well as their relationships with emotion-charged retroactive memory enhancement. Consistent with our hypotheses, we found that emotional learning retroactively enhanced memories for initially learnt neutral associations. This rapid retroactive enhancement was associated with prominent increases in trial-specific reactivation of initial learning activity in the hippocampus and stimulus-sensitive neocortex, as well as strengthening hippocampal coupling with the amygdala and related neocortical areas during emotional learning and hippocampal-neocortical offline reorganization during post-learning rests. Our findings suggest the neurobiological mechanisms by which memories for mundane neutral information can be enhanced when subsequently associated with emotionally arousing experiences, through rapid trial-specific reactivation of overlapping neural traces and hippocampal-neocortical functional reorganization involved in memory integration.

Figure 1 with 6 supplements see all
Experimental design and behavioral performance.

(A) The experiment consisted of three phases. During the initial learning phase, participants were instructed to vividly imagine each face and its paired object interacting with each other. During the emotional learning phase, each face from initial learning was presented again and then paired with either an aversive scream or a neutral voice. Participants were also instructed to imagine each face and its paired voice interacting. After a 30-min delay, participants performed a recognition memory test for face-object associations by matching each face (top) with one of the four objects (bottom). (B, C, D) Associative memory performance as a function of confidence ratings and emotional conditions in Study 1 (n=30), 2 (n=28), and 3 (fMRI, n=28). Bar graphs depict averaged correct proportion for face-object associations remembered with low (LC) and high (HC) confidence in aversive and neutral conditions separately. (E, F, G) High-confidence memory performance as a function of vividness ratings and emotional conditions in Study 1, 2, and 3 (fMRI). Bar graphs depict averaged correct proportion for face-object associations remembered with high confidence, which were rated with low (LV) and high (HV) vividness during the initial learning phase in aversive and neutral conditions separately. Error bars represent standard error of mean. ‘X’ indicates significant interaction (p<0.05). Notes: NS, non-significant; *p<0.05; two-tailed tests. The sample sizes are 30, 28 and 28 participants separately in Study 1, 2 and 3, unless otherwise noted in the following figure legends.

Results

Emotional learning retroactively enhances its related memory for initial neutral association

We first examined the emotion-charged retroactive effect for associative memory performance in two behavioral studies and one fMRI study separately. One-sample t-tests on each confidence level in the memory test revealed that the proportion of correctly remembered face-object associations with a 3 or 4 confidence rating (i.e. a four-point rating scale: 4 = ‘Very confident’, 1 = ‘Not confident at all’) was significantly higher than chance [(1/4+1/3+1/2+1)/4*100%=52%] across three studies (all p<0.05; see Figure 1—figure supplement 3 for statistics). However, the proportion of remembered associations with a 1 or 2 rating was not reliably higher than chance (see Figure 1—figure supplement 3 for statistics). It indicates that remembering with high confidence may reflect more reliable and stronger memories as compared to low confident remembrance. Thus, all remembered associations were then sorted into a high confident bin with 3 and 4 ratings, and a low-confident bin with 1 and 2 ratings likely reflecting guessing or familiarity.

Separate 2 (Emotion: aversive vs. neutral) by 2 (Confidence: high vs. low) repeated-measures ANOVAs were conducted for the three studies. For Study 1, this analysis revealed main effects of Emotion (F(1,29) = 5.18, p=0.030, partial η2=0.15) and Confidence (F(1,29) = 142.07, p<0.001, partial η2=0.83; Figure 1B). Although we only observed a trending Emotion-by-Confidence interaction effect in Study 1 (F(1,29) = 2.57, p=0.120, partial η2=0.08), it showed significant interaction effects in Studies 2 and 3 consistently (see below for statistics). Post-hoc comparisons revealed better memory for face-object associations only with high confidence in the aversive than neutral condition (t(29) = 2.18, p=0.037, dav = 0.22), but not with low confidence (t(29) = –0.15, p=0.883). Parallel analysis in Study 2 also revealed main effects of Emotion (F(1,27) = 5.53, p=0.026, partial η2=0.17) and Confidence (F(1,27) = 206.14, p<0.001, partial η2=0.88), and a significant Emotion-by-Confidence interaction (F(1,27) = 4.94, p=0.035, partial η2=0.16; Figure 1C). Post-hoc comparisons also revealed better associative memory with high confidence in the aversive (vs. neutral) condition (t(27) = 2.47, p=0.020, dav = 0.23), but not with low confidence (t(27) = –0.55, p=0.588). In Study 3 (fMRI), we again observed a similar pattern of results, including the main effect of Confidence (F(1,27) = 29.35, p<0.001, partial η2=0.52) and the significant Emotion-by-Confidence interaction (F(1,27) = 8.61, p=0.007, partial η2=0.24; Figure 1D), specifically with a retroactive enhancement on associative memory with high confidence in the aversive (vs. neutral) condition (t(27) = 2.59, p=0.015, dav = 0.10), but an opposite effect for associations with low confidence (t(27) = –2.18, p=0.038, dav = 0.24). These results indicate a reliable emotion-induced retroactive enhancement on related memories for neutral associations remembered with high confidence.

Besides confidence level for final memory performance, we also investigated whether the initial encoding strength of neutral associations modulates our observed emotion-induced retroactive effect. In order to examine how much distinct effect the initial encoding strength, as indicated by vividness rating during initial learning, would contribute to subsequent emotional memory benefit, we tested the trial-level relationship between vividness ratings and confidence ratings with a linear mixed-effects model. The linear mixed-effects model included all rating trials across participants, with participant as a random effect to reduce the bias of inherent correlation among ratings on the same participant (Gałecki and Burzykowski, 2013; Singh et al., 2021). This analysis revealed significantly positive but moderate correlations between vividness ratings (i.e. a four-point scale: 4 = ‘Very vivid’, 1 = ‘Not vivid at all’) and confidence ratings across three studies (Study 1: β=0.15, p<0.001, 95% CI = [0.07, 0.22]; Study 2: β=0.13, p<0.001, 95% CI = [0.07, 0.19]; Study 3: β=0.24, p<0.001, 95% CI = [0.16, 0.32]; Figure 1—figure supplement 4). These results indicate that extending beyond confidence rating, the vividness rating as a proxy for initial encoding strength might have a potentially distinct effect on subsequent emotional learning.

Subsequently, we further sorted the neutral associations remembered with high confidence into a high-vividness bin with 3 and 4 ratings, and a low-vividness bin with 1 and 2 ratings. Separate paired t-tests for the three studies revealed significantly better memory in the aversive (vs. neutral) condition, selectively for high-vividness encoded associations (Study 1: t(29) = 2.15, p=0.040, dav = 0.21; Study 2: t(27) = 2.16, p=0.040, dav = 0.25; Study 3: t(27) = 2.21, p=0.036, dav = 0.12), but not for low-vividness encoded associations (Study 1: t(29) = 0.27, p=0.793; Study 2: t(27) = –0.58, p=0.565; Study 3: t(27) = –0.40, p=0.696; Figure 1E–G). Parallel analyses were also conducted for those associations remembered with low confidence. However, we did not observe any reliable effect across three studies (see Figure 1—figure supplement 5 for statistics). Altogether, these results indicate that our observed emotion-induced retroactive benefit for related memory only occurs on relatively strong-encoded associations as indicated by high-vividness ratings.

In addition, to verify the elevation of autonomic arousal during emotional learning, we conducted a 2 (Emotion: aversive vs. neutral) by 2 (Phase: initial learning vs. emotional learning) repeated-measures ANOVA for skin conductance levels (SCLs) in the fMRI study (Study 3). This analysis revealed significant main effect of Phase (F(1, 19)=12.23, p=0.002, partial η2=0.39) as well as Emotion-by-Phase interaction (F(1, 19)=4.73, p=0.043, partial η2=0.20), but no main effect of Emotion (F(1, 19)=0.40, p=0.533, partial η2=0.02; Figure 1—figure supplement 6). Post-hoc comparisons revealed significantly higher SCL in the aversive than neutral condition during the emotional learning phase (t(19) = 2.23, p=0.038, dav = 0.50), but not during the initial learning phase (t(19) = –1.16, p=0.26). These results prove a higher level of automatic arousal successfully induced in the aversive (vs. neutral) condition during emotional learning, which potentially leads to a series of above-mentioned emotion-charged effects.

Emotional learning potentiates trial-specific reactivation of initial learning activity in the hippocampus and neocortex

Next, we investigated how emotional learning affects reactivation of initial learning activity for face-object associations cued by overlapping faces (Study 3). To assess reactivation of neural activity, we estimated the similarity of stimulus-evoked multi-voxel activation patterns between the initial learning phase and the subsequent emotional learning phase. Given our priori hypothesis, we focused on the hippocampus and stimulus-sensitive cortical regions including the lateral occipital complex (LOC) for object processing (Grill-Spector et al., 2001; Malach et al., 1995) and the face fusiform area (FFA) for face processing (Kanwisher et al., 1997). The bilateral hippocampal, bilateral ventral LOC (vLOC) and bilateral FFA ROIs were functionally identified by the overlapping area of two activation contrasts of face-object initial encoding and face-voice emotional encoding relative to fixation (i.e. all encoding trials vs. fixation during each learning phase; Figure 2—figure supplement 1), in order to specify the most engaged regions in both initial and emotional learning (i.e. activated and/or reactivated). The hippocampal ROI was further constrained by an anatomical mask of the bilateral hippocampus from the WFU PickAtlas. The vLOC and FFA ROIs were further constrained by a respective LOC or FFA mask, which is the overlapping area of an anatomical mask from the WFU PickAtlas (i.e. a composite AAL template including the bilateral middle occipital cortex, bilateral middle temporal cortex and bilateral fusiform gyrus for LOC, and an AAL template of the bilateral fusiform gyrus for FFA) and a functionally defined mask from the Neurosynth platform (i.e. ‘object recognition’ or ‘face recognition’ as a searching term; see Methods for details).

We conducted a condition-level pattern similarity analysis to investigate the overall emotion-charged neural reactivation in above three ROIs. As shown in Figure 2—figure supplement 2, we computed similarities between phases during the presentation of face-voice associations for each condition, while taking the presentation of face cues as an illustration purpose only. These analyses revealed a general emotion-induced increase in reactivation of initial learning activity in the hippocampus and stimulus-sensitive vLOC and FFA (see Figure 2—figure supplement 2 for statistics). In addition, we implemented a whole-brain exploratory analysis on condition-level reactivation with searchlight algorithm. Beyond confirming emotion-induced increased reactivation in the hippocampal, vLOC and FFA ROIs, the whole-brain searchlight analysis identified a set of other brain regions that exhibited greater neural reactivation in the aversive than neutral condition. These regions included the superior medial frontal cortex, insula, precuneus, and angular gyrus (Supplementary file 2).

To further examine whether the emotion-charged increase in neural reactivation is due to the trial-specific reinstatement or a broad category representation pattern, we conducted a set of trial-level pattern similarity analyses. As shown in Figure 2A (also see Figure 2—figure supplement 3), we computed the pair-specific similarity for each pair (i.e. correlation between each face-voice pattern during emotional learning and its corresponding face-object pattern during initial learning) to measure trial-specific reinstatement, and the across-pair similarity among different pairs (i.e., averaging all correlations between each face-voice pattern during emotional learning and other different face-object patterns during initial learning) to measure category-level representation. These similarity measures were then entered into separate 2 (Emotion: aversive vs. neutral) by 2 (Measure: pair-specific vs. across-pair) repeated-measures ANCOVAs for each ROI, with individual’s univariate activation differences (i.e. aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest. These analyses revealed significant main effects of Emotion in the hippocampus (F(1, 25)=9.48, p=0.005, partial η2=0.28) and vLOC (F(1, 25)=9.34, p=0.005, partial η2=0.27), as well as a significant Emotion-by-Measure interaction effect in the hippocampus (F(1, 25)=4.89, p=0.036, partial η2=0.16) and a similar trend but non-significant interaction in the vLOC (F(1, 25)=3.54, p=0.072, partial η2=0.12), but no main effect of Measure in the two ROIs (both F(1, 25)<0.60, p>0.440, partial η2<0.03; Figure 2B). Post-hoc comparisons (with covariates controlled) revealed significantly higher pair-specific similarity in both hippocampus (F(1, 25)=7.24, p=0.013, partial η2=0.22) and vLOC (F(1, 25)=8.28, p=0.008, partial η2=0.25) in the aversive than neutral condition. But it only revealed non-significant trends of higher across-pair similarity in the hippocampus (F(1, 25)=4.16, p=0.052, partial η2=0.14) and vLOC (F(1, 25)=3.37, p=0.078, partial η2=0.12) in the aversive (vs. neutral) condition. We also observed significantly higher pair-specific than across-pair similarity in the hippocampus (F(1, 25)=4.42, p=0.046, partial η2=0.15) and a similar but non-significant trend in the vLOC (F(1, 25)=2.96, p=0.097, partial η2=0.11) in the aversive condition, but not in the neutral condition (F(1, 25)<2.20, p>0.150, partial η2<0.08 in both ROIs). Parallel analysis for FFA pattern similarity revealed neither main effect nor interaction effect (all F(1, 25)<1.59, p>0.219, partial η2<0.06; Figure 2B). These results indicate that emotional learning prompts greater trial-specific reinstatement relative to category-level representation in the hippocampus, and it also leads to a similar but non-significant trend in the vLOC.

Figure 2 with 6 supplements see all
Trial-level neural reactivation of initial learning activity during emotional learning.

(A) An illustration of trial-level reactivation analysis. Example data was from one subject. During initial learning (left), sagittal views of activation maps for four trials were shown. During emotional learning (right), sagittal views of activation maps for the corresponding trials with two in aversive condition and two in neutral condition were shown. Solid lines indicate correlations for pair-specific similarity measure and dash lines indicate correlations for across-pair similarity measure. These correlations from each similarity measure were then averaged across trials for each participant in aversive and neutral conditions separately. (B) Bar graphs depict the average pair-specific and across-pair pattern similarities in aversive and neutral conditions for the bilateral hippocampus (left), bilateral ventral LOC (vLOC, middle) and bilateral FFA (right) ROIs. ‘X’ indicates a significant interaction (p<0.05). Error bars represent standard error of mean (n=28). (C) Scatter plots depict correlations of observed associative memory performance (i.e. remembered with high confidence) with predicted memory outcome from machine learning prediction analysis based on hippocampal pair-specific pattern similarity in aversive and neutral conditions. Dashed lines indicate 95% confidence intervals, and solid lines indicate the best linear fit. Dots represent data from each participant (n=28). Notes: ~p < 0.10; *p<0.05; **p<0.01; two-tailed tests.

Critically, we conducted a machine learning-based prediction analysis to examine the relationships of trial-specific reinstatement (i.e. pair-specific similarity) and category-level representation (i.e. across-pair similarity) with associative memory performance (i.e. remembered with high confidence). Individual’s pair-specific or across-pair similarity for each of aversive and neutral conditions was entered as an independent variable, and memory performance in the corresponding condition was entered as a dependent variable into separate linear regression models in the prediction analysis. These analyses revealed that hippocampal pair-specific similarity positively predicted memory in the aversive (r(predicted, observed)=0.46, p=0.008) but not neutral condition (r(predicted, observed)=–0.09, p=0.480; Figure 2C). Further Steiger’s test (Steiger, 1980) revealed a significant difference in correlation coefficients between aversive and neutral conditions (z=2.54, p=0.011). Hippocampal across-pair similarity positively predicted memory in both aversive (r(predicted, observed)=0.43, p=0.014) and neutral (r(predicted, observed)=0.50, p=0.001) conditions (Figure 2—figure supplement 5A), but with no significant difference between two conditions (z=–1.09, p=0.275). The prediction effects of hippocampal pair-specific (vs. across-pair) similarity on memory were non-significant in both conditions (both r(predicted, observed)<0.10, p>0.201; Figure 2—figure supplement 5B). These results reveal an emotion-specific predictive effect of trial-specific reinstatement (as indicated by pair-specific similarity) for memory in the hippocampus, supporting the emotion-charged retroactive memory benefit. However, reliable category-level representation (as indicated by across-pair similarity) in the hippocampus predicts better memory regardless of emotion modulation, suggesting a general benefit on overall memory.

Additionally, to investigate whether the consistency of activity pattern during each phase accounts for the memory benefit as an alternative explanation, we computed the within-encoding similarity (i.e. averaging all correlations among face-object patterns during the initial learning phase), and the within-arousal similarity (i.e. averaging all correlations among face-voice patterns during the emotional learning phase) in aversive and neutral conditions separately (Figure 2—figure supplement 3). Machine learning-based prediction analyses revealed no any reliable relationship of within-encoding similarity (aversive: r(predicted, observed)=–0.20, p=0.676; neutral: r(predicted, observed)=–0.11, p=0.543; Figure 2—figure supplement 6A) or within-arousal similarity (aversive: r(predicted, observed)=–0.28, p=0.960; neutral: r(predicted, observed)=–0.28, p=0.960; Figure 2—figure supplement 6B) with associative memory performance (i.e. also remembered with high confidence) in both conditions. These results indicate that our observed emotion-charged retroactive memory enhancement is not related to the reliability or consistency of activity pattern within each phase.

Emotional learning enhances hippocampal coupling with the amygdala and neocortex which predicts associative memory for initial neutral events

To investigate how emotional learning modulates functional interactions of the hippocampus and its related neural circuits involved in memory integration, we conducted a task-dependent psychophysiological interaction (PPI) analysis for the emotional learning phase to assess functional connectivity of the hippocampal seed with every other voxel of the brain (Figure 3A). In line with our priori hypothesis, we mainly focused on significant clusters in the right amygdala (Figure 3B), left middle portion of FFA (mFFA; Figure 3C) and left superior portion of LOC (sLOC; Figure 3D), which showed greater functional coupling with the hippocampus in the aversive (vs. neutral) condition during emotional learning (see Figure 3—figure supplement 1 for visualization; Supplementary file 3). The amygdala ROI was further constrained by an anatomical mask of the bilateral amygdala from the WFU PickAtlas. The mFFA and sLOC ROIs were also constrained separately by the FFA or LOC mask, same with above pattern similarity analysis (see Methods). We also conducted a parallel control PPI analysis for the initial learning phase. This analysis revealed no any reliable emotional effect (i.e. aversive vs. neutral) during initial learning in the three ROIs identified above (see Figure 3—figure supplement 1 for visualization). These results indicate that emotional learning induces functional connectivity changes with prominent increases in hippocampal-amygdala and hippocampal-neocortical coupling.

Figure 3 with 3 supplements see all
Hippocampal connectivity with the amygdala and neocortical regions during emotional learning phase accounting for emotion-charged retroactive memory enhancement.

(A) The bilateral hippocampal seed used in task-dependent gPPI functional connectivity analysis (i.e. the same hippocampal ROI used in pattern similarity analysis). (B–D) Significant clusters in the right amygdala, left middle FFA (mFFA) and left superior LOC (sLOC) regions, showing greater functional coupling with the hippocampus in the aversive (vs. neutral) condition during emotional learning (n=28). (E–G) Bar graphs depict averaged hippocampal connectivity with the amygdala, mFFA and sLOC for face-object associations remembered with high confidence and forgotten in aversive and neutral conditions separately. Error bars represent standard error of mean (n=27, one participant had no forgotten trial in the neutral condition). (H–J) Scatter plots depict correlations of observed memory performance (i.e. remembered with high confidence) with predicted memory outcome from prediction analysis based on hippocampal-amygdala, hippocampal-mFFA and hippocampal-sLOC connectivity (i.e. remembered with high confidence vs. forgotten) in the aversive condition. Dashed lines indicate 95% confidence intervals, and solid lines indicate the best linear fit. Dots represent data from each participant (n=28). (K) The mediating effect of hippocampal-mFFA/sLOC connectivity on the positive association between hippocampal-amygdala connectivity and emotion-charged associative memory (n=28). Paths are marked with standardized coefficients. Solid lines indicate significant paths. Notes: ~p < 0.08; *p<0.05; **p<0.01; ***p<0.001; two-tailed tests.

We then investigated how emotion-charged hippocampal connectivity contributes to the retroactive benefit on associative memory. Data extracted from hippocampal connectivity with the amygdala and stimulus-sensitive neocortical regions (i.e. mFFA and sLOC) during the emotional learning phase were submitted to separate 2 (Emotion: aversive vs. neutral) by 2 (Memory: remembered with high confidence vs. forgotten) repeated-measures ANOVAs. The main effects of Emotion were expected, as these three ROIs were defined by the emotional contrast of aversive relative to neutral condition. Thus, we mainly examined whether such emotion-enhanced hippocampal connectivity is associated with the retroactive memory benefits, by focusing on the main effect of Memory and Emotion-by-Memory interaction. This analysis revealed significant main effects of Memory in hippocampal connectivity with the mFFA (F(1, 26)=5.59, p=0.026, partial η2=0.18; Figure 3F) and sLOC (F(1, 26)=4.40, p=0.046, partial η2=0.15; Figure 3G), but not in hippocampal-amygdala connectivity (F(1, 26)=0.21, p=0.65, partial η2=0.008; Figure 3E). There was no reliable Emotion-by-Memory interaction showing in the hippocampal connectivity with these three ROIs (all F(1,26) < 0.73, p>0.400, partial η2<0.03; Figure 3E–G).

Given the modulatory effects of emotion for hippocampal connectivity reported in previous studies (de Voogd et al., 2017; Richardson et al., 2004) and emotion-charged neural reactivation in relation to associative memory obeserved in our study, we assumed that hippocampal connectivity, which works in concert with reactivation to promote memory integration (Schlichting and Preston, 2014; Sutherland and McNaughton, 2000), might also show some emotion-specific relationship with associative memory. Although we did not find reliable interaction effect in above ANOVA analyses, we employed the machine learning-based prediction analysis to investigate whether greater hippocampal coupling during emotional learning could predict better memory and whether this relationship would show emotional specificity. Interestingly, we found that greater hippocampal connectivity with mFFA and sLOC (i.e. remembered with high confidence relative to forgotten) were predictive of better associative memory (i.e. remembered with high confidence) in the aversive condition (mFFA: r(predicted, observed)=0.57, p<0.001; sLOC: r(predicted, observed)=0.65, p<0.001; Figure 3H–J), but not in the neutral condition (both r(predicted, observed)<0.15, p>0.160; Figure 3—figure supplement 2). A similar trend (though not significant) was also shown in hippocampal-amygdala connectivity (i.e. remembered with high confidence relative to forgotten; aversive: r(predicted, observed)=0.24, p=0.076; neutral: r(predicted, observed)=–0.25, p=0.839). Further Steiger’s tests revealed significant differences in correlation coefficients between two conditions for hippocampal coupling with mFFA (z=2.13, p=0.033) and sLOC (z=2.15, p=0.032), and a non-significant trend of difference for hippocampal-amygdala coupling (z=1.79, p=0.073). These results indicate that emotion-charged hippocampal connectivity with stimulus-sensitive neocortical regions positively predicts associative memory in the aversive but not neutral condition, implying an emotional specificity effect, though hippocampal-amygdala connectivity only shows a similar trend but non-significant effect.

Emotion-charged retroactive memory enhancement is associated with hippocampal connectivity with the amygdala and neocortex during emotional learning

The modulatory role of the amygdala on hippocampal dialogue with the neocortex is recognized to promote more efficient information transmission and communication between the hippocampus and related neocortical regions (Alvarez et al., 2008; Hermans et al., 2017; Phelps and LeDoux, 2005), which could further lead to emotion-charged memory enhancement. According to our empirical observations, memory performance in the aversive condition showed only a trending positive correlation with hippocampal-amygdala connectivity, but highly positive correlations with hippocampal-mFFA/sLOC connectivity. Therefore, we assumed a potential mediatory pathway among these variables, that is, hippocampal-amygdala connectivity would indirectly account for emotion-charged memory performance through the mediation of hippocampal-mFFA/sLOC connectivity. Based on above theoretical motivation as well as empirical observations, we further implemented an exploratory mediation analysis to investigate how the hippocampus, amygdala and neocortical systems during emotional learning work in concert with each other to support emotion-charged retroactive memory enhancement.

Specifically, we constructed a mediation model with hippocampal-amygdala connectivity (i.e. remembered with high confidence relative to forgotten) as input variable, hippocampal-mFFA and -sLOC connectivity (i.e. remembered with high confidence vs. forgotten) as two separate mediators and individual’s associative memory performance (i.e. remembered with high confidence) as outcome variable in the aversive condition (Figure 3K). This exploratory mediation analysis revealed two significant mediating effects on the positive relationship between hippocampal-amygdala connectivity and memory outcome (hippocampal couplings with mFFA and sLOC: Indirect Est = 0.19, p=0.010, 95% CI = [0.044, 0.328], and hippocampal–sLOC coupling: Indirect Est = 0.20, p=0.016, 95% CI = [0.036, 0.360]). That is, hippocampal-amygdala connectivity affected hippocampal-sLOC connectivity through a partial mediating effect of hippocampal-mFFA connectivity, which could ultimately account for emotion-charged memory performance in the aversive condition. Notably, we also conducted additional control analyses with a set of alternative mediation and modulation models in the aversive condition (Figure 3—figure supplement 3). These analyses, however, did not reveal any reliable mediating or modulating effect, indicating that our data did not support such alternative models. Since there was no any significant relationship between hippocampal connectivity and associative memory in the neutral condition, we did not conduct mediation analysis in this condition. Altogether, these exploratory results indicate that increased hippocampal-amygdala coupling might indirectly account for emotion-charged associative memory, likely mediating by hippocampal-neocortical couplings during emotional learning.

Emotion-charged retroactive memory enhancement is associated with the reorganization of hippocampal connectivity during post-learning rests

Furthermore, to explore how offline hippocampal connectivity changes at resting states prior to and after emotional learning contribute to emotion-charged retroactive memory benefit, we implemented seed-based correlational analyses of resting-state fMRI data separately for three rest scans that interleaved initial and emotional learning phases, with the hippocampal ROI as seed to assess its functional connectivity with the rest of the brain (Figure 4A). We computed the difference maps of hippocampal connectivity during Rest 2 relative to Rest 1 to reflect an initial-learning-related preparatory memory network primed to be engaged in subsequent emotional learning, and connectivity during Rest 3 relative to Rest 2 to reflect the following emotional-learning-related changes in the memory network. Thereafter, we conducted multiple regression analyses to compute these connectivity difference maps in relation to associative memory performance (i.e. remembered with high confidence) in aversive versus neutral conditions, which is analogous to the interaction effects between post-learning hippocampal connectivity changes (i.e. Rest 2 vs. 1 or Rest 3 vs. 2) and conditions (i.e. aversive vs. neutral). For hippocampal connectivity changes after initial learning relative to baseline (i.e. Rest 2 vs. 1), we identified a significant cluster in the left inferior portion of LOC (iLOC) that was also constrained by the above-mentioned LOC mask with both anatomical and functional criteria (Figure 4B; Supplementary file 4). For connectivity changes after emotional learning relative to before (i.e. Rest 3 vs. 2), we identified significant clusters in a set of brain regions including the bilateral inferior parietal lobule (IPL) extending into the angular gyrus (AG), the right posterior cingulate cortex (PCC), the right anterior prefrontal cortex (aPFC) and the right medial prefrontal cortex (mPFC) (Figure 4C; Supplementary file 4). Specifically, the exploratory whole-brain analyses revealed that hippocampal-iLOC connectivity at post-initial-learning rest positively correlated with associative memory in the aversive condition, but negatively correlated with memory in the neutral condition (see Figure 4—figure supplement 1A for visualization). We also observed that hippocampal connectivity with transmodal prefrontal-parietal regions (i.e. IPL, PCC, aPFC and mPFC) at post-emotional-learning rest positively correlated with memory in the aversive condition, but negatively in the neutral condition (see Figure 4—figure supplement 1B for visualization). As a complement, the machine learning-based prediction analyses for post-learning hippocampal connectivity changes revealed very similar patterns of positive predictions for associative memory in the aversive condition (iLOC: r(predicted, observed)=0.47; IPL: r(predicted, observed)=0.54; PCC: r(predicted, observed)=0.51; aPFC: r(predicted, observed)=0.52; mPFC: r(predicted, observed)=0.48; all p<0.005 while controlling for memory in the neutral condition), but negative predictions in the neutral condition (iLOC: r(predicted, observed)=–0.45; IPL: r(predicted, observed)=–0.55; PCC: r(predicted, observed)=–0.50; aPFC: r(predicted, observed)=–0.53; mPFC: r(predicted, observed)=–0.51; all p<0.006 while controlling for memory in the aversive condition). These exploratory results indicate a potential shift of post-learning hippocampal connectivity from the object-sensitive lateral occipital complex to more distributed transmodal prefrontal and posterior parietal areas, which predicts emotion-charged retroactive memory benefit.

Figure 4 with 1 supplement see all
Hippocampal- and parahippocampal-neocortical connectivity at post-learning rests in relation to emotion-charged retroactive memory enhancement.

(A) An illustration of hippocampal-seeded functional connectivity analyses, and sagittal views of hippocampal connectivity maps at the group level for three rest scans (n=27, data from one participant during Rest 2 was incomplete due to hardware malfunction). (B) Significant cluster in the left inferior LOC (iLOC), showing its greater connectivity with the hippocampus at Rest 2 (vs. Rest 1) in positive relation to memory in the aversive but not neutral condition. The iLOC ROI was constrained by the LOC mask, same with pattern similarity analysis. (C) Significant clusters in the bilateral inferior parietal lobule (IPL), the right anterior prefrontal cortex (aPFC), the right posterior cingulate cortex (PCC) and the right medial prefrontal cortex (mPFC), showing their greater connectivity with the hippocampus at Rest 3 (vs. Rest 2) in positive relation to memory in the aversive but not neutral condition. (D) A task-unrelated parahippocampal seed used in parallel control connectivity analyses (n=27), showing no reliable cluster under above-mentioned contrasts. Notes: Color bar represents T values.

Additionally, we conducted parallel control analyses with a task-unrelated parahippocampal region as seed. This parahippocampal sensory region engaged in scene processing is not related to the stimuli used in our study. The control analyses revealed no reliable clusters survived for the task-unrelated parahippocampal connectivity during post-learning rests in relation with emotion-charged retroactive memory benefit (Figure 4D). It indicates that our observed offline hippocampal-neocortical functional reorganization is a special process involving with stimuli- and memory-related regions (i.e. iLOC, IPL, PCC, aPFC, and mPFC), but not with irrelevant brain regions (i.e. the task-unrelated parahippocampal region).

Discussion

By three studies, we investigated the neurobiological mechanisms of how memories for associated mundane events are retroactively modulated by following learning of emotional events. As expected, emotional learning retroactively enhanced memory for initial neutral associations. This rapid retroactive enhancement was associated with increased trial-specific reactivation of initial learning activity in the hippocampus and stimulus-sensitive neocortex, as well as strengthened hippocampal coupling with the amygdala and neocortical regions during emotional learning. Complementally, hippocampal-amygdala coupling positively predicted the emotion-charged retroactive memory benefit, mediating by increased hippocampal-neocortical interactions. Moreover, we explored a potential shift of hippocampal-neocortical connectivity contributing to the emotion-charged retroactive memory enhancement during post-learning rests, from local stimulus-sensitive neocortex to more distributed transmodal prefrontal and posterior parietal regions. Our findings suggest that emotional learning retroactively promotes the integration for relevant neutral events into episodic memory to foster prediction of future events, through stimulating trial-specific reactivation of overlapping memory traces and reorganization of related memories with their updated values in an integrated network.

Emotion-charged retroactive enhancement on memory integration for initial neutral events

Behaviorally, we observed that emotional learning retroactively enhanced associative memory for initial neutral events. This rapid memory enhancement occurred in the aversive rather than neutral condition, and appeared to be selective for face-object associations strongly encoded as indicated by high-vividness ratings during initial learning. Notably, such enhancement effect was reproducible across two independent studies indicating its robustness and reliability. This is in line with findings from previous studies on sensory preconditioning, suggesting that a salient event such as fear could spread significant value to its associated mundane event and prioritize memory of this associated event for future use (Li et al., 2008; Shohamy and Daw, 2015; Wimmer and Shohamy, 2012). Beyond these studies focusing on item memory of indirectly associated event, our results provide novel evidence that the value of subsequent emotional event could also generalize into the initial associations of past neutral events, most likely through trial-specific reactivation of overlapping memory traces and reorganization of related memories into a more tightly integrated network.

Using an adapted sensory preconditioning paradigm, our observed emotion-charged retroactive memory benefit differs from the conventional behavioral tagging effects by three following aspects. First, such sensory preconditioning paradigm allows us to investigate trial-specific enhancement for each pair by associative learning task (Wang and Kahnt, 2021; Wimmer and Shohamy, 2012), in which the overlapping face cues were presented during both initial and emotional learning phases, rather than a general effect with non-overlapping stimuli in each phase (Clewett et al., 2022; Dunsmoor et al., 2015). Second, consistent with the sensory preconditioning models (Kurth-Nelson et al., 2015; Sharpe et al., 2017), we observed the retroactive memory enhancement in the immediate test, which differs from the delayed benefit pertaining to behavioral tagging literature (Ballarini et al., 2009; Dunsmoor et al., 2012; Dunsmoor et al., 2011). Third, our observed retroactive benefit was selective for strongly encoded associations as indicated by high-vividness ratings (presumably strong memories)(Holmes et al., 2022), which contradicts with the rescued effect for initial weak memories expected by behavioral tagging models (Dunsmoor et al., 2015; Ritchey et al., 2016; ). Neutral associations with high-vividness ratings were, at least temporarily, encoded into memory during the initial learning phase. Their memory traces could be subsequently reactivated by corresponding face cues, and further modulated by integrating with arousal during the emotional learning phase. However, associations with low-vividness ratings might be encoded relatively weaker. Since no specific memory traces were formed for these low-vividness associations, they could not be reactivated nor modulated later. It is thus conceivable that the emotion-charged trial-specific retroactive memory integration tends to occur on relatively strong associations. Together, our findings suggest that emotional learning with autonomic arousal occurring during reactivation of initial overlapping memory traces, may stimulate a more targeted and rapid memory reorganization through an integrative encoding mechanism.

Emotion-charged trial-specific reactivation of overlapping neural traces in the hippocampus and neocortex

In parallel with above-described rapidly and selectively retroactive memory enhancement, our imaging results showed transient increases in trial-specific reactivation of initial learning activity in the aversive (vs. neutral) condition during the emotional learning phase. Firstly, our results from condition-level similarity analyses between initial and emotional learning phases demonstrate greater reactivation occurred after the onset of aversive (vs. neutral) voices. Such effect might result from emotion-induced autonomic arousal accompanying with elevated catecholamine release, that rapidly potentiates the excitability of overlapping neuronal ensembles and strengthens the reactivation of initial memory (Wong et al., 2019; Zhou et al., 2009). More interestingly, results from our trial-level similarity analyses between phases further suggest that such emotion-charged increased reactivation mostly reflect the trial-specific reinstatement rather than a board category representation. Indeed, compared with the general predictive effect of category-level representation on overall memory regardless of emotional conditions, such trial-specific reinstatement positively predict memory performance for initial neutral associations only in the aversive rather than neutral condition, suggesting a specificity of emotional modulation on trial-specific reinstatement but not on category-level representation. Based on memory allocation and integration models, reactivation of overlapping neuronal ensembles may serve as a mechanism by which memories for related prior and present experiences can be allocated into an integrated network of representations (Schlichting and Frankland, 2017; Silva et al., 2009). By this view, autonomic arousal triggered by emotional learning may stimulate trial-specific reactivation of initial encoding activity, which could strengthen the specific association between prior memories for neutral information in the integrated memory network. Moreover, we also computed within-phase similarities to measure the consistency of activity pattern during initial and emotional learning phases separately. However, there was no significant relationship between within-phase similarities and memory performance. These results reveal that our observed emotion-charged retroactive memory enhancement does not result from the consistency of activity patterns.

Critically, we found the prominent emotional enhancement for trial-specific reactivation (i.e. pair-specific similarity) in the hippocampus and vLOC, but not in the FFA. The hippocampus is known to play a key role in the integration of information processed in distributed neocortical regions (i.e. LOC and FFA) into episodic memory through a pattern completion process (Kuhl et al., 2010; Shohamy and Wagner, 2008). Thus, hippocampal reactivation may strengthen the coherence of related representations throughout our brain (Staudigl and Hanslmayr, 2018; Wimmer and Shohamy, 2012). Consistently, we indeed observed a positive correlation between emotion-charged trial-specific reactivation in the hippocampus and memory for face-object associations. The object-sensitive vLOC showed an increase in trial-specific reactivation of object stimuli, which might reflect reinstatement of corresponding initial face-object associations during emotional learning (Hofstetter et al., 2012; Tambini et al., 2010). But the face-sensitive FFA could not provide pure evidence of reactivation since face information was presented during both phases. These results suggest task-related regional specificity of the observed emotion-charged trial-specific reactivation. In addition, the trending but not significant emotional effects for category representation (i.e. across-pair similarity) found in the hippocampus, vLOC and FFA might be due to more attention in the aversive (vs. neutral) condition generally attracted by the screaming stimuli, rather than trial-specific reactivation. This explanation is also supported by our condition-level similarity results, showing strengthened reactivation only occurred after the onset of aversive (vs. neutral) voices during emotional learning. Altogether, our findings suggest that emotional learning may induce transient increases in trial-specific reactivation of overlapping neural traces in the hippocampus and stimulus-sensitive neocortical regions (i.e. vLOC). This emotion-charged trial-specific reactivation shows emotional and regional specificity in relation to memory integration, which may promote a rapid memory reorganization of related events.

Emotion-charged memory reorganization via hippocampal-amygdala-neocortical interactions

Coinciding with transient increases in trial-specific reactivation, our results further suggested an emotion-charged memory reorganization by stimulating functional connectivity among the hippocampus, amygdala, and neocortical circuits during emotional learning, as well as a potential shift of hippocampal-neocortical connectivity from local stimulus-sensitive occipital area to more distributed prefrontal and posterior parietal systems during post-learning resting state.

Four aspects of our data support this interpretation. First, we observed emotion-induced increases in hippocampal functional connectivity with the amygdala and face/object-sensitive neocortical regions (i.e. mFFA and sLOC) in the aversive (vs. neutral) condition during emotional learning, but not initial learning, phase. Second, although such hippocampal connectivity patterns did not show reliable Emotion-by-Memory interaction effect, results from our prediction analyses revealed that hippocampal connectivity with the neocortical regions during emotional learning positively predicted memory for face-object associations in the aversive rather than neutral condition. Consistent with emotion-induced increases in trial-specific reactivation, these results point toward the specificity of emotional modulation on the relationships between hippocampal connectivity and associative memory performance. Third, we explored that increased hippocampal-neocortical coupling could mediate the positive relationship between hippocampal-amygdala coupling and emotion-charged retroactive memory enhancement. This exploratory mediation effect suggests that emotional arousal directly paired with face cues could induce greater hippocampal-amygdala connectivity acting on hippocampal-FFA connectivity during emotional learning, which then stimulated the cued trial-specific reactivation in the hippocampus and LOC through increasing hippocampal-LOC interaction, and ultimately contributed to retroactive memory benefit for related events. These results are in line with previous studies that the modulatory role of amygdala could support more efficient information transmission and communication between the hippocampus and related neocortical regions (i.e. the FFA and LOC) (Hamann, 2001; Hermans et al., 2014), and hippocampal–neocortical functional coordination plays a critical role in reactivation and integration of episodic memories (Kuhl et al., 2010; Schlichting and Preston, 2014; Sutherland and McNaughton, 2000; Wimmer and Shohamy, 2012). Thus, the observed emotion-charged hippocampal-amygdala-neocortical interactions are most likely to link with rapid trial-specific reactivation of past neutral experiences and reorganize their memories into a network with integrated representations. Our findings demonstrate a mechanism of emotion-induced memory reorganization via strengthened hippocampal–neocortical functional coupling, coinciding with the modulation of amygdala activity.

Last but not least, we explored a potential hippocampal-neocortical functional reorganization during post-learning rests predictive of emotion-charged retroactive memory benefit, with a shift away from local stimulus-sensitive LOC to more distributed prefrontal and posterior parietal regions including the aPFC, mPFC, PCC and IPL expanding into the AG. Initial learning elicited greater hippocampal-iLOC connectivity during post-initial-learning rest, which might reflect a possible increase in neural excitability of this circuit. Since neuronal excitability in the hippocampal-neocortical circuitry prior to encoding is recognized to modulate subsequent allocation and integration of newly acquired information into long-term memory (Josselyn and Frankland, 2018; Kaefer et al., 2022; Schlichting and Frankland, 2017; van Dongen et al., 2011; Yoo et al., 2012), we thus speculate that greater hippocampal-iLOC connectivity might provide a preparatory state to allocate new information into existing memory traces during emotional learning and thus contribute to emotion-charged retroactive memory benefit. Besides, neural circuits activated by emotional learning exhibit persistent activity and also alter a series of systems-level interactions during post-emotional-learning rest (de Voogd et al., 2016; Hermans et al., 2017; Murty et al., 2017), including hippocampal connectivity with aPFC, mPFC, PCC, and IPL observed in this study. These transmodal prefrontal-parietal regions are core nodes of the default mode network (DMN) that is recognized to play a crucial role in remembering past events and simulating possible future use (Andrews-Hanna et al., 2010; Schacter et al., 2011; Spreng and Schacter, 2012). Thus, our observed offline hippocampal-neocortical connectivity changes may contribute to emotion-charged retroactive memory benefit, likely through functional reorganization mechanisms of memory-related brain systems at both pre- and post-emotional-learning phases. Interestingly, hippocampal-neocortical connectivity changes involved in this reorganization positively correlated with memory in the aversive condition, but negatively correlated in the neutral condition (i.e. partial correlations controlling for memory in the other condition). It is possible to speculate that the emotion-charged retroactive memory benefit might not only reflect an enhancement of emotion-related information but also a suppression of other neutral information. These findings point toward a potential trade-off between prioritization of emotion-related memory and neglection of mundane neutral memory, aligning with the activity of locus coeruleus-norepinephrine system during post-encoding periods (Clewett and Murty, 2019).

Taken together, our findings suggest that emotional learning not only rapidly strengthens online task-dependent hippocampal-neocortical coupling through amygdala modulation, but it also potentiates offline hippocampal-neocortical reorganization integrating memory traces into more distributed networks and prioritizing them for future use. In line with emotion-charged retroactive memory benefit and increased trial-specific reactivation for associated events, our observed hippocampal connectivity changes during emotional learning and post-learning rests most likely reflect a relational process underlying memory integration, but not an indiscriminate generalization or persistence of emotional arousal (Hermans et al., 2017; Tambini and Davachi, 2013; Tambini et al., 2010). It points toward a rapid emotion-modulated reorganization mechanism, by which memories for neutral events can be updated according to the significance of subsequent emotional events. This emotion-modulated reorganization not only contributes to the integration of past and current experiences into episodic memory, but also supports future simulations.

Limitations

Although our study provides converging evidence of rapid neural reactivation and connectivity reorganization underlying emotion-charged retroactive benefit on memory integration, several limitations should be considered. First, given our experimental design including an initial learning followed by an emotional learning and a surprise memory test, it is thus possible that subsequent emotional learning might overwrite the effect of initial learning on final memory performance. This could account for no reliable correlation of post-initial-learning hippocampal connectivity changes (i.e. Rest 2 vs. Rest 1) with either neutral or average memory performance. Future studies are required to test memory before emotional learning to disentangle post-learning signatures linked to pure memory effect for neutral events and emotion-charged memory effect separately. Second, our moderate sample size in fMRI Study 3 would be underpowered to detect individual differences in across-subject correlations and mediation analyses. A larger sample size would reinforce the reproducibility of brain-behavior correlations in future studies. Third, our functionally defined ROIs in the hippocampal connectivity analysis during the emotional learning phase may bring potential selection bias. Future studies with anatomical ROIs would help mitigate this issue. Forth, it is challenging to reliably separate neural signals associated with ‘face cues’ and ‘face-voice associations’ due to only an interval of 2 s. Future design with longer and jittered intervals may resolve this issue.

Conclusion

Our study demonstrates that emotional learning can retroactively promote memory integration for preceding neutral events through an emotion-modulated rapid reorganization mechanism, characterized by transient increases in trial-specific reactivation of overlapping neural traces, strengthened hippocampal-neocortical coupling modulated by the amygdala during emotional learning, and a shift of hippocampal-neocortical connectivity from local stimulus-sensitive neocortex to distributed prefrontal and posterior parietal areas during post-learning rests. Our findings across three independent studies advance the understanding of neurobiological mechanisms by which emotion can reshape our episodic memory of previous neutral events to foster its priority for future use, and also provide novel insights into maladaptive generalization in mental disorders like PTSD.

Methods

Participants

A total of 89 young, healthy college students participated in three separate studies. In Study 1, 30 participants (16 females; mean age ±s.d., 22.23±2.05 years old, ranged from 18 to 26 years) were recruited from Beijing area to participate in a behavioral experiment. In Study 2 by an independent research staff, 28 participants (14 females; mean age ±s.d., 21.83±1.93 years old, ranged from 18 to 26 years) were recruited from Xinyang city in Henan province for a replication experiment to ensure the reliability of our behavioral findings from Study 1. In Study 3, another independent cohort of 31 participants (17 females; mean age ±s.d., 22.55±2.25 years old, ranged from 18 to 27 years) was recruited from Beijing area to participate in an event-related fMRI experiment. Data from three participants were excluded from further analyses due to either falling asleep during fMRI scanning (n=2) or poor memory performance (i.e. the overall memory accuracy across confidence ratings and conditions was almost 0; n=1). The sample sizes across three studies were estimated by a power analysis using G*Power 3.1, which yielded the power of around 85–90% (i.e. from 26 to 30 participants) for a moderate repeated-measures ANOVA, consistent with many memory studies (Gruber et al., 2016; Liu et al., 2016; Meyer and Benoit, 2022; Schlichting and Preston, 2014; Wimmer and Shohamy, 2012). In Study 3, the sample size of 28 valid participants could give us power more than 70% for a moderate correlation (i.e. r>0.45) (Cohen, 1992; Cohen, 2013).

All participants were right-handed with normal hearing and normal or corrected-to-normal vision, reporting no history of any neurobiological diseases or psychiatric disorders. Informed written consent was obtained from each participant before the experiment. The Institutional Review Board for Human Subjects at Beijing Normal University (ICBIR_A_0098_002), Xinyang Normal University (same as above) and Peking University (IRB#2015-09-04) approved the procedures for Study 1, 2, and 3, respectively.

Materials

One hundred and forty-four face images (72 males and 72 females) were carefully selected from a database with color photographs of Chinese individuals unknown to participants (Chen et al., 2012), under following criteria suggested by previous studies: direct gaze contact, no headdress, no glasses, no beard, etc (Qin et al., 2007). There was also no strong emotional facial expression in these faces, and no significant difference in terms of arousal, valence, attractiveness, and trustworthiness between male and female faces according to rating results in a previous study (Liu et al., 2016). Seventy-two object images were obtained from a website (http://www.lifeonwhite.com) or publicly available resources on the internet (Dunsmoor et al., 2015). All objects were common in life with neutral valence. Four short clips of female voices were carefully selected from an audio source website (https://www.smzy.com/), with two aversive screams serving as emotional arousal manipulation and two neutral voices (i.e. ‘Ah’ and ‘Eh’) as control. Acoustic characteristics of the four voice clips were measured and controlled using Praat ( http://www.praat.org/), including duration (2 s), frequency (in Hertz) and power (in decibel) (Supplementary file 1). An independent cohort of 19 participants (10 females; mean age ±s.d., 21.90±2.35 years old, ranged from 18 to 26 years) was recruited from local area to participate in a pilot experiment, which was performed to rate the four voices before the formal memory studies. Participants were instructed to listen to each voice, and then rate valence and arousal separately for the voice on a 9-point self-rating manikin scale (i.e. 9 = ‘Extremely pleasant’ or ‘Extremely arousing’, 1 = ‘Not pleasant at all’ or ‘Not arousing at all’). Two aversive screams had significantly higher arousal and lower valence than neutral voices (all p<0.001; Figure 1—figure supplement 1). There was no significant difference in arousal or valence between two aversive (or neutral) voices (all p>0.05; Figure 1—figure supplement 1).

Faces were randomly split into two sets of 72 images with half male and half female faces for each participant: one list was paired with objects to create 72 face-object pairs for the initial learning phase, and the other was served as foils for face recognition memory test [2 (Emotion: aversive vs. neutral) by 2 (Confidence: high vs. low)] repeated-measures ANOVAs on face item memory revealed neither main effect of Emotion (Study 1: F(1, 29)=3.53, p=0.070; Study 2: F(1, 26)=0.001, p=0.970; Study 3: F(1, 27)=0.09, p=0.765) nor Emotion-by-Confidence interaction effect (Study 1: F(1, 29)=1.75, p=0.196; Study 2: F(1, 26)=0.62, p=0.439; Study 3: F(1, 27)=1.08, p=0.307), but only significant main effect of Confidence (Study 1: F(1, 29)=141.11, p<0.001, partial η2=0.83; Study 2: F(1, 26)=133.29, p<0.001, partial η2=0.84; Study 3: F(1, 27)=40.82, p<0.001, partial η2=0.60). These results indicate that the modulatory effect of emotional learning did not present on face item memory, but might directly enhance face-object associative memory through increasing reactivation (see Figure 1—figure supplement 2 for details). Then, each face in face-object pairs was randomly paired with one of four voices to create 72 face-voice pairs and assigned into either an aversive condition (i.e. 36 faces paired with aversive screams) or a neutral condition (i.e. 36 faces paired with neutral voices) during the emotional learning phase.

Experimental procedures

In each of the three studies, the experimental design consisted of three consecutive phases: an initial learning, a followed-up emotional learning, and a surprise recognition memory test (Figure 1A). During the initial learning phase, participants were instructed to view 72 face-object pairs in an incidental encoding task. During the emotional learning phase, faces from the initial learning phase were presented again as cues and paired with either an aversive or a neutral voice. After a 30-min delay, participants performed a surprise recognition memory test for face-object associations. In Study 3, participants underwent fMRI scanning with concurrent recording of skin conductance while they were performing initial learning and emotional learning phases interleaved by three rest scans. Specifically, the fMRI experiment began with an 8 min baseline rest scan (i.e. Rest 1), followed by the initial and emotional learning phases. Each of the two learning phases was followed by another 8 min rest scan (i.e. Rest 2 and 3). During rest scans, participants were shown a black screen and instructed to keep awake with their eyes open. Finally, the surprise associative memory test was performed outside the scanner.

Initial learning task

During initial learning, 72 faces were randomly paired with 72 objects to create 72 face-object associations for each participant. Each association was centrally presented on the screen for 4 s, and followed by a vividness rating scale for 2 s. To ensure incidental memory encoding, participants were instructed to vividly imagine each face interacting with its paired object and give a vividness rating on their imagined scenario on a 4-point Likert scale (i.e. 4 = ‘Very vivid’, 1 = ‘Not vivid at all’). Trials were interleaved by a fixation with an inter-trial interval jittered from 2 to 6 s (i.e. 4 s on average with 2 s step). The total of 72 face-object pairs were viewed twice, which were split into two runs with 12 min each.

Emotional learning task

During emotional learning, each face from initial learning was presented at the center of screen for 2 s, and then followed by concurrent presentation of the same face paired with either an aversive or a neutral voice for another 2 s. To ensure the consistency with initial incidental learning task, participants were again instructed to imagine the face interacting with its paired voice and give their vividness rating on a 4-point Likert scale for 2 s. After that, each trial was followed by a relatively long inter-trial interval jittered from 6 to 10 s (i.e. 8 s on average with 2 s step), to reduce potential contamination of voice-induced emotional arousal among neighboring trials. Totally 72 face-voice pairs were presented only once in a pseudo-randomized order that no more than 2 voices from the same condition (i.e. aversive or neutral condition) appeared in a row. The emotional learning phase lasted 16.8 min.

Surprise recognition memory test

After a 30-min delay, participants were instructed to perform a self-paced recognition memory test for face-object associations. Each trial consisted of four faces from initial learning on the top of screen and their corresponding objects randomly located on the bottom of screen. A total of 72 learned face-object pairs were randomly sorted into 18 slides with 4 pairs each. Participants were asked to pair each face with one of the four objects according to their remembrance of face-object associations, and then gave their confidence rating for each pair separately on a 4-point scale (i.e. 4 = ‘Very confident’, 1 = ‘Not confident at all’). Participants were required to make choice for each face in an order from left to right, and carefully recall before they made the choice to avoid errors.

Behavioral data analysis

Participants’ behavioral performances on vividness rating in the initial learning phase (i.e. the second run/viewing), memory accuracy and confidence rating in the memory test were analyzed using Statistical Product and Service Solutions (SPSS, version 22.0, IBM) and R (version 4.2.1). Two conditions were created according to emotional learning manipulations. During initial learning, trials for face-object associations subsequently paired with aversive screams were assigned into the aversive condition, and remaining trials with neutral voices were assigned into the neutral condition. During emotional learning, trials paired with aversive screams were assigned into the aversive condition, and trials with neutral voices were assigned into the neutral condition. One sample t-tests were conducted to examine the reliability of face-object associative memory performance as compared to the chance level for each confidence level (i.e. from 1 to 4). Given that participants were required to match 4 sets of face-object pairings within one screen at a time in the memory test, the chance level of associative memory performance was 52% calculated using the mean accuracy of 4 pairings [(1/4+1/3+1/2+1)/4*100%]. Then, all remembered associations were sorted into a high-confident bin with levels 3 and 4 reflecting reliable memory, and a low-confident bin with levels 1 and 2 reflecting guessing or familiarity (Squire et al., 2007). Separate 2 (Emotion: aversive vs. neutral) by 2 (Confidence: high vs. low) repeated-measures ANOVAs were conducted in the three studies to examine the emotion-charged effect on reliable memory performance rather than guessing or familiarity. Moreover, trial-level relationship between vividness ratings during initial learning and confidence ratings for final memory performance was tested by a linear mixed-effects model, in order to examine how much distinct effect the initial encoding strength of face-object associations (as indicated by vividness rating) would contribute to subsequent emotional memory benefit (Gałecki and Burzykowski, 2013; Singh et al., 2021). These remembered associations were further sorted into a high-vividness bin with levels 3 and 4, and a low-vividness bin with levels 1 and 2. We conducted paired t-tests between aversive and neutral conditions on memory accuracy with high-vividness and low-vividness ratings separately, to investigate how initial memory strength modulates our observed emotion-charged effect. Given the robust results were all found in high-confidence memory performance across the three studies, we thus used the high-confidence memory as a reliable measure of memory performance in the following reactivation and connectivity analyses.

Skin conductance recording and analysis

Skin conductance was collected to assess autonomic arousal induced by aversive screaming (vs. neutral) voices during the emotional learning. It was recorded simultaneously with fMRI scanning using a Biopac MP 150 System (Biopac, Inc, Goleta, CA). Two Ag/AgCl electrodes filled with isotonic electrolyte medium were attached to the center phalanges of the index and middle fingers of each participant’s left hand. The gain set to 5, the low-pass filter set to 1.0 Hz, and the high-pass filters set to DC (Indovina et al., 2011). Data were acquired at 1000 samples per second and transformed into microsiemens (μS) before further analyses. Given the temporal course of skin conductance in response to certain event, mean skin conductance levels (SCLs) were calculated for a period of 6 s after each stimulus onset.

Imaging acquisition

Whole-brain imaging data were collected on a 3T Siemens Prisma MR scanner (Siemens Medical, Erlangen, Germany) with a 20-channel head coil system at Peking University in Beijing, China. Functional images were collected using a multi-band echo-planar imaging (mb-EPI) sequence (slices, 64; slice thickness, 2 mm; TR, 2000 ms; TE, 30ms; flip angle, 90°; multiband accelerate factor, 2; voxel size, 2×2×2 mm; FOV, 224×224 mm; 240 volumes for each of the three rest scans, 365 and 508 volumes for the initial and emotional learning scans separately). To correct for distortions, field-map images (i.e. magnitude and phase images) were acquired (slices, 64; slice thickness, 2 mm; TR, 635ms; TE1, 4.92ms; TE2, 7.38ms; flip angle, 60°; voxel size, 2×2×2 mm; FOV, 224×224 mm). Structural images were acquired through three-dimensional sagittal T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence (slices, 192; slice thickness, 1 mm; TR, 2530ms; TE, 2.98ms; flip angle, 7°; inversion time, 1100ms; voxel size, 1×1×1 mm; FOV, 256×256 mm).

Imaging preprocessing

Brain imaging data was preprocessed using Statistical Parametric Mapping (SPM12; http://www.fil.ion.ucl.ac.uk/spm). The first 4 volumes of functional images were discarded for signal equilibrium. Remaining images were firstly corrected for distortions related to magnetic field inhomogeneity. Subsequently, these functional images were realigned for head-motion correction and corrected for slice acquisition timing. Each participant’s images were then co-registered to their own T1-weighted anatomical image, spatially normalized into a standard stereotactic Montreal Neurological Institute (MNI) space and resampled into 2 mm isotropic voxels. Finally, images were smoothed with a 6 mm FWHM Gaussian kernel. A high-pass filter (1/128 Hz cutoff) was also applied to remove low-frequency signal drifts.

Regions of interest (ROIs) definition

To investigate the emotion-charged reactivation in both condition- and trial-level pattern similarity analyses, we identified the bilateral hippocampal, bilateral ventral LOC (vLOC) and bilateral FFA ROIs by the overlapping area of two group-level univariate activation contrasts of face-object association encoding (i.e. initial learning) and face-voice association encoding (i.e. emotional learning) separately relative to fixation (i.e. all encoding trials vs. fixation during each learning phase), using a stringent height threshold of p<0.0001 and an extent threshold of p<0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Miller et al., 2022; Nichols and Hayasaka, 2003; Figure 2—figure supplement 1). The vLOC was further constrained by a LOC mask, which is the overlapping area of the combined anatomical automatic labeling (AAL) template including the ‘bilateral middle occipital cortex’, ‘bilateral middle temporal cortex’, and ‘bilateral fusiform gyrus’ from the WFU PickAtlas toolbox (Grill-Spector et al., 2001; Kourtzi and Kanwisher, 2001; Malach et al., 1995), and the mask derived from the Neurosynth platform for large-scale, automated synthesis of fMRI data (http://neurosynth.org/) with ‘object recognition’ as a searching term (p<0.01 with FDR correction). The FFA ROI was further constrained by a FFA mask, which is the overlapping area of the AAL template of ‘bilateral fusiform gyrus’ from the WFU PickAtlas toolbox (Kanwisher et al., 1997) and the mask from the Neurosynth platform with ‘face recognition’ as a searching term (p<0.01 with FDR correction).

The above-defined hippocampal ROI was also used as a seed to further investigate emotion-induced changes in hippocampal functional connectivity. For task-dependent hippocampal connectivity analysis during initial and emotional learning phases, the right amygdala, left middle portion of FFA (mFFA) (Visconti di Oleggio Castello et al., 2021) and left superior portion of LOC (sLOC) (Barbieri et al., 2019; Olivo et al., 2019) ROIs were defined using a group-level connectivity contrast of the aversive relative to neutral condition during the emotional learning phase, by a height threshold of p<0.005 and an extent threshold of p<0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Nichols and Hayasaka, 2003). For post-encoding hippocampal connectivity analysis during three resting phases, significant clusters in the left inferior portion of LOC (iLOC) (Barbieri et al., 2019; Olivo et al., 2019) as well as the bilateral IPL, right PCC, right aPFC and right mPFC were derived from the group-level multiple regression analyses on connectivity contrast maps (i.e. Rest 2 vs. 1, and Rest 3 vs. 2) with interaction effects between aversive and neutral conditions, by the same threshold criterion as task-dependent connectivity analysis above. Same with pattern similarity analysis, the mFFA, sLOC and iLOC ROIs were further constrained separately by the above-mentioned FFA or LOC mask.

A task-unrelated parahippocampal region, as a seed in parallel control post-encoding connectivity analyses, was defined by the bilateral posterior parahippocampal gyrus using the WFU PickAtlas toolbox. However, the posterior parahippocampal area is not only a key sensory region for scene processing, but also involved in the encoding of associations into an integrated representation (Qin et al., 2007). To avoid the interference of its association-encoded function, we further restricted the posterior parahippocampal area by removing its overlapping area with two group activation contrasts of encoding during initial and emotional learning separately relative to fixation (i.e. all face-object/face-voice encoding trials vs. fixation during each learning phase), by a relatively less stringent height threshold of p<0.01 and an extent threshold of p<0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions (Nichols and Hayasaka, 2003).

ROI-based pattern similarity analysis

To measure the neural reactivation of initial learning activity for face-object associations cued by the overlapping faces during emotional learning, we computed multi-voxel pattern similarity of stimulus-evoked activation between initial and emotional learning phases in each ROI. We analyzed the pattern similarity on condition level to determine an overall emotional effect of reactivation by comparing aversive and neutral conditions. Two separate GLMs were conducted for the initial learning phase and the emotional learning phase. In each GLM, two regressors of interest were modeled for trials with 2 s from onset of each stimulus (i.e. face-object associations during initial learning or face-voice associations during emotional learning) in aversive and neutral conditions, and convolved with the canonical hemodynamic response function (HRF) in SPM12. In the GLM of emotional learning phase, the 2 s presentations of face cues prior to the onset of face-voice associations in aversive and neutral conditions were also included as two separate regressors of no interest. Additionally, each participant’s motion parameters from the realignment procedure were included in each GLM to regress out effects of head movement on brain response. Then, t values of spatial activation maps in each ROI for aversive and neutral conditions in the initial learning phase and the emotional learning phase (i.e. during the presentation of face cues and the following presentation of face-voice associations separately) were extracted into separate vectors. We eliminated the mean activation from each pattern by z-scoring across voxels of each ROI. Thus, the resulting mean-centered pattern has relative voxel amplitudes (i.e. voxel-level variability) preserved without any difference in the overall amplitudes, ensuring that the subsequent similarity results are fully attributable to the pattern itself (Coutanche, 2013; Tompary and Davachi, 2017). Similarity between vectors of initial and emotional learning phases was computed for each condition and presentation using Pearson’s correlation, and then Fisher-transformed. The resultant similarity values for each ROI were entered into a 2 (Emotion: aversive vs. neutral) by 2 (Presentation: face cue vs. face-voice association) repeated-measures ANOVA.

To further examine whether the emotional effect of reactivation is contributed by the trial-specific reinstatement or a broad category representation, we conducted a set of trial-level multi-voxel pattern similarity analyses. We modelled each trial with 2 s from onset of the stimulus (i.e. face-object association collapsing across two repetitions during initial learning or face-voice association during emotional learning) as a separate regressor, convolved with the canonical hemodynamic response function (HRF) in SPM12. In the GLM of emotional learning phase, the presentation of all face cues with 2 s duration was also included as a regressor of no interest. Other procedures were the same with GLMs in the condition-level analysis above. This resulted in 72 regressors in the GLM of initial learning phase and 73 regressors in the GLM of emotional learning phase. T values of spatial activation pattern in each ROI for each trial were extracted into a separate vector and z-scored. Thereafter, similarity between vectors of initial and emotional learning phases was also computed for each condition and measure in each ROI using Pearson’s correlation, and then Fisher-transformed.

We computed three measures of trial-level pattern similarity in aversive and neutral conditions separately (Figure 2—figure supplement 3): (1) pair-specific similarity (i.e. correlation between each face-voice pattern and its corresponding face-object pattern), (2) across-pair within-condition similarity (i.e. average correlation between each face-voice pattern and all other different face-object patterns within the same aversive/neutral condition) and (3) across-pair between-condition similarity (i.e. average correlation between each face-voice pattern and all other different face-object patterns from the different condition). We then conducted separate 2 (Emotion: aversive vs. neutral) by 3 (Measure: pair-specific vs. across-pair within-condition vs. across-pair between-condition) repeated-measures ANCOVAs in each ROI, with individual’s univariate activation differences (i.e. aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest (see Figure 2—figure supplement 4 for statistics).

To directly examine whether across-pair pattern similarity measures for within- and between-condition pairs show different effects, we further conducted separate 2 (Emotion: aversive vs. neutral) by 2 (Measure: across-pair within-condition vs. across-pair between-condition) repeated-measures ANCOVAs for each ROI, with above-mentioned covariates of no interest. This analysis revealed only a trending but non-significant main effect of Emotion (hippocampus: F(1, 25)=4.16, p=0.052, partial η2=0.14; vLOC: F(1, 25)=3.37, p=0.078, partial η2=0.12; FFA: F(1, 25)=3.96, p=0.058, partial η2=0.14), and neither main effect of Measure nor Emotion-by-Measure interaction effect for each ROI (all F(1, 25)<2.00, p>0.170; Figure 2—figure supplement 4). These results indicate no reliable emotional modulation effect on both across-pair similarity measures, and no significant difference between values of the two across-pair similarities. We thus combined these two across-pair similarities (i.e. averaging all correlations in both across-pair within- and between-condition) to quantify a generally category-level representation pattern. To better characterize the relationships between trial-specific reinstatement (i.e. pair-specific similarity) and category-level representation (i.e. across-pair similarity), we conducted separate 2 (Emotion: aversive vs. neutral) by 2 (Measure: pair-specific vs. across-pair) repeated-measures ANCOVAs in each ROI, also with individual’s univariate activation differences (i.e. aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest to further mitigate the potential interference of overall activation from pattern results.

To investigate whether the consistency of activity pattern within each phase also accounts for the emotion-induced memory benefit, we computed two other trial-level pattern similarity measures in aversive and neutral conditions separately (Figure 2—figure supplement 3): (1) within-encoding similarity (i.e. average correlation among face-object patterns within the initial learning phase), and (2) within-arousal similarity (i.e. average correlation among face-voice patterns within the emotional learning phase). These two within-phase similarity measures were computed in a same approach as between-phase similarity measures above. Thereafter, we conducted machine learning-based prediction analyses of these two similarity measures with associative memory performance.

Whole-brain pattern similarity analysis

A searchlight mapping method was implemented to assess the reactivation of initial learning activity during emotional learning on the whole-brain level. Similar to above ROI-based analysis on condition level, we computed multi-voxel pattern similarity between initial and emotional learning phases for aversive and neutral conditions separately in each searchlight, using a 6 mm spherical ROI centered on each voxel across the whole brain. The resultant Fisher-transformed searchlight maps for two conditions were then entered into a paired-t test (i.e. aversive vs. neutral) on the group-level analysis to determine other brain regions involved in emotion-induced increased reactivation. Significant clusters were identified from the group analysis using a height threshold of p<0.005 and an extent threshold of p<0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Nichols and Hayasaka, 2003).

Task-dependent functional connectivity analysis

To assess hippocampus-based functional connectivity associated with emotional learning, we conducted a generalized form of task-dependent psychophysiological interaction (gPPI) analysis during initial and emotional learning phases separately (Friston et al., 1997; Xiong et al., 2021). This analysis examined condition-specific modulation on functional connectivity of a specific seed (i.e. the hippocampal ROI here) with the rest of the brain, after removing potentially confounding influences of overall task activation and common driving inputs. The physiological activity of given seed region was computed as the mean time series of all voxels. They were then deconvolved to estimate neural activity (i.e. physiological variable), and multiplied with the task design vector by contrasting aversive and neutral conditions (i.e. psychological variable) to form a psycho-physiological interaction vector. This interaction vector was convolved with a canonical HRF to form the PPI regressor of interest. The psychological variable representing the task conditions (i.e. aversive and neutral conditions) was also included in the GLM to remove out the effects of common driving inputs on brain connectivity. Contrast images corresponding to PPI effect (i.e. aversive vs. neutral) at the individual-subject level were then entered into the group-level analysis. Significant clusters were identified from the group analysis using a height threshold of p<0.005 and an extent threshold of p<0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Nichols and Hayasaka, 2003).

To further investigate the effect of emotion-charged hippocampal functional connectivity on associative memory, we conducted an additional hippocampal-seeded gPPI analysis only during the emotional learning phase by taking Memory Status (i.e. forgotten vs. remembered with high confidence) into account. All procedures were same as the above gPPI analysis, except that we included four PPI regressors of interest (i.e. forgotten in aversive condition, forgotten in neutral condition, remembered with high confidence in aversive condition, remembered with high confidence in neutral condition) into the model. To ensure the reliability of associative memory performance, trials later remembered with low confidence regardless of Emotion conditions were not included in the model. Mean t values extracted from the resultant contrast images in each ROI were then submitted to a 2 (Emotion: aversive vs. neutral) by 2 (Memory Status: forgotten vs. remembered with high confidence) repeated-measures ANOVA.

Task-free functional connectivity analysis

To assess emotion-induced changes in post-encoding hippocampal functional connectivity, we conducted seeded correlational analyses of resting-state fMRI data for three rest scans separately (i.e. Rest 1, 2, and 3) . Regional time series within the hippocampal seed were extracted from bandpass-filtered images with a temporal filter (0.008–0.10 Hz), and then submitted into the individual level fixed-effects analyses. Six motion parameters as well as cerebrospinal fluid and white matter of each participant that account for potential physiological noise and movement-related artifacts were regarded as covariates of no interest. To mitigate potential individual differences in baseline connectivity, hippocampal-seeded connectivity map at Rest 1 was subtracted from Rest 2 (i.e. Rest 2 vs. 1), and connectivity map at Rest 2 was subtracted from Rest 3 (i.e. Rest 3 vs. 2) for each participant. The resultant connectivity maps were then submitted separately into the second-level multiple regression models, with memory accuracy scores in the aversive and neutral conditions (i.e. memory with high confidence) as two separate covariates of interest. We conducted a contrast to test the difference in regression coefficients between aversive and neutral conditions – that is the interaction effect of post-encoding hippocampal connectivity changes (i.e. Rest 2 vs. 1 or Rest 3 vs. 2) in relation to memory performance and emotional conditions (i.e. aversive and neutral). Significant clusters were determined using the same criterion with above gPPI analyses. Additionally, we also conducted control analyses with a task-unrelated parahippocampal region as seed of interest. All procedures were same as the above hippocampal-seeded analysis.

To visualize the relationships between post-encoding hippocampal connectivity changes and memory performance in aversive and neutral conditions, mean t values extracted from the resultant contrast images in each ROI and their corresponding memory accuracies (i.e. memory with high confidence) were plotted into partial correlations without further statistical inferences.

Prediction analysis

We used a machine learning-based prediction algorithm with balanced fourfold cross-validation to confirm the robustness of relationships of reactivation and functional connectivity with memory performance. This prediction analysis complements conventional correlation models which are sensitive to outliers and have no predictive value (Cohen et al., 2010; Geisser, 1993; Qin et al., 2014). Individual’s reactivation (or functional connectivity) index was entered as an independent variable, and their corresponding memory performance was entered as a dependent variable. Data for these two variables were divided into fourfolds. A linear regression model was built using data from three out of the four folds and used to predict the remaining data in the left-out fold. This procedure was repeated four times to compute a final r(predicted, observed). Such r(predicted, observed), representing the correlation between the observed values of the dependent variable and the predicted values generated by the linear regression model, was estimated as a measure of how well the independent variable predicted the dependent variable. Finally, a nonparametric approach was used to test the statistical significance of the model, by generating 1000 surrogate data sets with randomly shuffled participant labels, under the null hypothesis of r(predicted, observed) (Cohen et al., 2010). The statistical significance (i.e. p value) was determined by measuring the percentage of generated surrogate data greater than the true correlation.

Mediation analysis

We conducted a mediation analysis to further explore how emotional learning affects initial associative memory through functional amygdala-hippocampal-neocortical pathways during emotional learning using Mplus 7.0 software (https://www.statmodel.com/index.shtml) (Hayes et al., 2011). Mediation models were constructed to investigate how hippocampal-neocortical (i.e. mFFA and sLOC) connectivity mediated the influence of hippocampal-amygdala connectivity on associative memory performance in the aversive condition. We used hippocampal-amygdala connectivity as the predictor, associative memory performance as the outcome, and hippocampal-mFFA and -sLOC connectivity as two separate mediators. In this model, individual’s hippocampal connectivity with each targeted ROI (i.e. amygdala, mFFA and sLOC) was measured with mean t values extracted from the corresponding contrast images of gPPI analysis (i.e. remembered with high confidence vs. forgotten). Individual’s associative memory performance was measured with correctness proportion for face-object associations remembered with high confidence. The mediating effect of hippocampal-neocortical connectivity was tested by a bias-corrected bootstrap with 1,000 samples, which could improve the sensitivity and robustness of statistical estimates in small-to-moderate samples (Preacher and Hayes, 2008; Shrout and Bolger, 2002; Tian et al., 2021). Both direct and indirect effects of hippocampal-amygdala connectivity on associative memory were estimated, which generated percentile based on confidence intervals (CI).

Estimates of effect size

Effect sizes reported for ANOVAs are partial eta squared, referred to in the text as η2. For paired t-tests, we calculated Cohen’s d using the mean difference score as the numerator and the average standard deviation of both repeated measures as the denominator (Lakens, 2013). This effect size is referred to in the text as dav, where ‘av’ refers to the use of average standard deviation in the calculation.

Data availability

All fMRI data collected in this study are available on OpenNeuro under the accession number ds004109 (https://doi.org/10.18112/openneuro.ds004109.v1.0.0). All code used for analysis are available on GitHub (https://github.com/QinBrainLab/2017_EmotionLearning.git, copy archived at swh:1:rev:da02cb17f27adb21652f3fc878f4bd39e5b88e38).

The following data sets were generated
    1. Zhu YN
    2. Zhang L
    3. Zeng YM
    4. Chen C
    5. Fernández G
    6. Qin S
    (2022) OpenNeuro
    Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization.
    https://doi.org/10.18112/openneuro.ds004109.v1.0.0

References

    1. Brogden WJ
    (1939) Sensory pre-conditioning
    Journal of Experimental Psychology 25:323–332.
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  1. Book
    1. Gałecki A
    2. Burzykowski T
    (2013) Linear mixed-effects models using r
    In: Gałecki A, editors. Linear Mixed-Effects Models Using R. Springer. pp. 245–273.
    https://doi.org/10.1007/978-1-4614-3900-4
    1. Hayes AF
    2. Preacher KJ
    3. Myers TA
    (2011)
    Mediation and the estimation of indirect effects in political communication
    Research Sourcebook for Political Communication Research 23:434–465.

Decision letter

  1. Thorsten Kahnt
    Reviewing Editor; National Institute on Drug Abuse Intramural Research Program, United States
  2. Laura L Colgin
    Senior Editor; University of Texas at Austin, United States
  3. Vishnu Murty
    Reviewer; Temple University, United States
  4. Arielle Tambini
    Reviewer; Nathan Kline Institute for Psychiatric Research, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for sending your article entitled "Emotional tagging retroactively promotes memory integration through rapid neural reactivation and reorganization" for peer review at eLife. Your article is being evaluated by 3 peer reviewers, and the evaluation is being overseen by a Reviewing Editor and Laura Colgin as the Senior Editor.

As you can see, although reviewers felt that your study was well-designed and the results novel and interesting, they had a number of concerns. Most notably, they requested additional analyses that (a) demonstrate that item rather than category information was reactivated (R1's point 3, R2's point 1), (b) show evidence for the regional specificity of these effects (R3's point 4), and (c) test the interaction in the connectivity analysis to show that coupling predicts memory for aversive but not neutral items (R1's point 5; also see R2's points 2 for different but related issues). Given that your study is potentially underpowered to detect individual differences (R3's point 1), reviewers were concerned that these analyses are not possible in your data. Please let us know how you plan to respond to these and the other comments raised by reviewers.

Reviewer #1:

Zhu and colleagues present a manuscript detailing neural mechanisms driving associative memory enhancements for previously neutral information using an acquired equivelance paradigm. The authors show greater associative memory for face-word object pairs, when the face is later paired with an aversive event. Further, these memory enhancements were associated with increased interactions between the amygdala, hippocampus, relevant-sensory cortex, and higher-order associative cortex both during encoding and post-encoding rest. The question is quite timely, and the results provide a nice extension of rodent work. The analysis plan was well designed, and the results were highly interesting. However, there were some weaknesses in the methodological approach as well and the interpretation of the data that derail from the overall impact. These concerns are detailed below.

1. While the authors provide a great mechanistic understanding of retroactive memory effects, the framing around behavioral tagging and studies related to Dunsmoor et al., (2015) seem inaccurate. In the behavioral tagging literature, the focus is on retroactive memory effects for information that is un-related to the arousing events. By using a sensory pre-conditioning paradigm, the same stimuli are shown during both the pre-arousal phase and the arousal phase. The authors tend to conflate sensory preconditioning with behavioral tagging. This is especially relevant as the authors show immediate memory benefits which are more consistent with sensory pre-conditioning rather than behavioral tagging. With that in mind, I think the introduction needs to be significantly re-worked and more work needs to be done integrating the findings with human sensory pre-conditioning literature (i.e., Wimmer & Shohamy 2012).

2. Related to the above point, I think two additional factors need to be addressed when thinking about interpreting the data through the lens of behavioral tagging versus sensory preconditioning. The first is that effects were occurring during an immediate memory test, as behavioral tagging is thought to support delayed effects. The second would be a moderating role for memory strength during phase 1, perhaps by using the vividness ratings. Behavioral tagging should produce larger effects on weak items (i.e., low vividness), while sensory preconditioning should produce larger effects on strong items (i.e., high vividness).

3. While I found the RSA results to be quite interesting, I had a few methodological concerns that made me question their validity. First, in general affective information can increase activation in many of the ROIs of interest in a non-specific way, including the FFA and LO. The authors should re-run their analyses controlling for univariate activation. Second, I think the RSA comparisons could be more specific and better controlled. Rather than collapsing across the entire condition, the RSA analysis would be more powerful run on an individual-trial level. Thus, the comparisons would be RSA between a pair of trials in phase 1 and 2, and then that trial in comparisons to all other trials within the same condition and all other trials in the other condition. This analysis would show whether a specific item is being reinstated or a more broad category representation.

4. There is a growing body of work characterizing post-encoding mechanisms contributing to memory benefits, in particular emotional memory benefits, that should be included in the discussion (see DeVoogd, Tambini, Murty, Hermans).

5. The post-encoding analyses need to have additional controls. Ideally, the authors would run an interaction analysis to characterize regions in which post-encoding coupling predicts Emotional but not Neutral memory. Further, control analyses in sensory regions that are not directly related to the stimuli (such as PPA) would be beneficial.

Reviewer #2:

The manuscript by Zhu and colleagues examines the retroactive influence of emotional 'tags' on associative memory for tagged items, providing new behavioral evidence for a retroactive memory benefit associated with emotional arousal. This is replicated across a few behavioral cohorts and is importantly distinct from prior demonstrations that emotion can enhance item memory for material that is conceptually related to 'tagged' information. fMRI is used to examine possible neural mechanisms for the retroactive benefit: greater reactivation of initial learning patterns is present for the emotionally tagged condition, and hippocampal interactions are enhanced in this condition as well. There is also evidence for post-tagging resting hippocampal interactions that predict the emotional-tagging memory benefit, suggesting that both mechanisms at the time of 'tagging', and subsequent mechanisms may mediate this behavioral effect. I think the main results in the manuscript are sound and advance the literature. I think there a few points that need to be considered, however.

1. The reinstatement effect (greater for emotional vs. neutral trials) is a compelling demonstration that is consistent with the retroactive memory benefit. A couple of points to consider regarding this analysis:

a. The current approach creates an average pattern for 'aversive' and 'neutral' stimulus pairs, both during tagging and initial learning. Thus, greater reinstatement for the aversive condition likely reflects category-level reinstatement of face/object information present during learning, rather than the specific reinstatement of a particular pair (which is implied in the discussion, e.g. "stimulating reactivation of overlapping memory traces" on line 414). Did the authors also examine evidence for pair-specific reinstatement (i.e. greater similarity between the tagging of Face A and encoding trial of Face A, vs. the tagging of Face A and encoding of Face B)? Evidence for trial-specific reinstatement would provide a tighter mechanistic link between reactivation and enhanced associative memory associated with emotional tagging.

b. At face value, the relationship between reinstatement evidence and associative memory (in Figure 2D) helps to link reinstatement with subsequent memory. However, given that the relationship is not specific (present for both emotional and neutral conditions) it makes me wonder whether there are alternative explanations beyond reactivation explaining better memory. Since reinstatement is a simple relationship between across-trial encoding and tagging phases, it is possible that participants with less reliable or consistent activity patterns during either the encoding or tagging phase alone (due to fluctuations in attention or other factors) may have worse memory, which may drive the correlation. This can be addressed by (1): showing that the similarity within encoding, i.e. across trials or conditions is not predictive of memory, (2): the same for the tagging phase, and/or (3): by examining evidence for pair-specific reinstatement as described above, which is a more specific measure.

2. The differences in hippocampal interactions based on emotional tagging (Figure 3D,E) reveal greater coupling with the amygdala and FFA/LOC during emotional vs. neutral 'tagging', consistent with the retroactive emotional memory enhancement. The further analyses in Figure 3F,G indicate, however, that these differences are not specifically linked with the emotion-related memory enhancement and instead show main effects of memory and emotion (not an interaction). Despite this lack of differences due to the emotional memory enhancement (across trials) the authors examine whether individual differences in these measures are related to emotionally-tagged memory.

a. Please provide logic linking these analyses – why was it expected that these measures would predict emotionally-tagged memory across subjects when trial-level effects relating to emotional memory (specifically) were not found?

b. Please also show the correlations that are described in the text (lines 327-335). It is a bit surprising to see the come up as boxes in the SEM analysis (Figure 3H) although they are not shown individually first. Do the correlations between memory and hippocampal connectivity (w/ amygdala and FFA/LOC) significantly differ between the emotionally-tagged and neutral conditions? The correlations are significant for the emotional condition, but not the neutral condition, implying specificity although this is not shown/tested. Or, the analysis can be done with the difference in memory for emotional vs. neutral conditions to gain this specificity, which was used for the rest analyses in Figure 4.

c. Lastly, logic should also be provided for constructing the SEM in the specific manner it was setup (hippocampal-cortical connectivity serving as the mediating variable between hipp-amygdala connectivity and memory).

3. The primary rest analysis – which examines changes in hippocampal connectivity (rest-3 minus rest-2) that are related to individual differences in emotionally-tagged memory enhancement – is clear and well-motivated. However, the logic underlying the first analysis, which relates changes in connectivity from rest-1 to rest-2 (baseline to post-initial-learning rest) to the emotionally-tagged memory enhancement, is not clear. Given that emotional tagging occurs after rest-2, there should be no meaningful signature associated with biased consolidation of this material prior to this time point. I understand that the authors are trying to show how emotional tagging, per se, alters post-encoding consolidation signatures during rest above and beyond those that are present after initial learning alone. I infer that the goal is to compare the post-tagging rest to an analysis that illustrates simple relationships between immediate post-encoding connectivity and later memory (not related to tagging). To achieve this goal with the current design, I would suggest a more straightforward analysis of examining changes in hippocampal connectivity from rest-1 to rest-2 that are related to individual differences in associative memory that are not related to tagging, such as the average across neutral and emotionally-tagged stims (or perhaps neutral stimuli that are not emotionally tagged). Average associative memory would likely be the best measure, considering that emotional tagging may enhance memory for related material at the cost of impaired memory for non-tagged material. There is clear logic in this analysis, in that it would reveal initial post-encoding resting connectivity that is related to memory and NOT the emotional tagging procedure. The contrast of this kind of analysis with the rest-3 minus rest-2 analysis would then be clear. Note that the main analysis (examining changes from rest-2 to rest-3) on its own somewhat controls for initial post-encoding activity (captured in rest-2), so perhaps this other analysis is not needed (although the claims made in the Discussion would have to be altered).

Reviewer #3:

In this paper, Zhu and colleagues report a series of studies (2 behavioral and 1 fMRI) designed to test the retroactive effects of aversive associations-- which they call "emotional tagging"-- on memory for previously-learned, overlapping neutral associations. Across the 3 studies, they found that neutral face-object associations were remembered better if the face was later paired with an aversive sound. In the fMRI study, the authors additionally showed that the tagging phase was associated with greater pattern reactivation when new associations were aversive, compared to neutral. They also found changes in functional connectivity with the hippocampus that were associated with the retroactive effects of tagging, both during the tagging phase itself and during a post-tagging resting-state scan.

This paper had several strengths. The behavioral findings were compelling, showing the same general pattern across the 3 studies. The topic is timely, as there is accumulating evidence for the retroactive effects of emotion on memory, but little neural data explaining such effects in humans. The analysis approach included multiple sophisticated methods.

However, there were also significant weaknesses that limit the impact of the paper.

1. The first major weakness is the sample size of the fMRI study (effective N=28), coupled with the fact that many of the conclusions were based on correlations with individual differences in memory-- including the findings that hippocampal reactivation and changes in functional connectivity predicted retroactive memory benefits. This is a concern because correlations across small sample sizes are less likely to replicate in future work. The mediation model is subject to these same concerns.

2. The second major weakness is a lack of theoretical clarity regarding the mechanisms supporting the retroactive memory benefit. Two main ideas are introduced: the idea of behavioral tagging (i.e., as related to synaptic tag-and-capture models) and the idea that reactivation plays an important role in memory allocation and integration. I see these as two separate theoretical perspectives that could make different predictions here, which may have been the authors' intention. However, they are not clearly set up as competing hypotheses, nor are they clearly integrated into a unified account. This made it difficult to interpret the results in light of either account.

3. The terms "reorganization" and "reconfiguration" are used to describe the functional connectivity results. I have seen these terms used to refer to network-level changes, but they overstate the functional connectivity differences here, which are simply changes in the maps resulting from a seed-based functional connectivity analysis.

4. It would be more informative to examine pattern reactivation in face-selective and object-selective areas separately (here, they appear to be lumped together), since only reactivation of object-selective areas should be taken as "pure" evidence of learning-phase reactivation, due to the overlap in faces shown during the learning and tagging phases. If there's an increase in pattern similarity in face-selective areas, this could be explained by changes in attention to the visual stimuli associated with the aversive sound rather than reactivation per se.

5. The prediction approach was a nice addition here, in that it can help to determine which effects are robust across subjects. However, it appears that they were based on clusters that were already deemed significant through conventional statistical analyses (e.g., see lines 794-795), which suggests that they are biased by the group effects.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization" for further consideration by eLife. Your revised article has been evaluated by Laura Colgin (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Reviewers felt that the primary results reported in your manuscript are sound but that some of the details of the later analyses were not fully transparent which made those harder to evaluate. Please respond to the points raised by the individual reviewers.

Reviewer #1:

The authors did a truly outstanding job responding to the concerns that I raised in the first review. I sincerely believe that the new introduction and discussion provide a much more emperically-motivated depiction of the prior literature, and does a stand-out job delineating these findings from other related concepts such as behavioral tagging.

I was also excited to see such clear results from the additional analyses run. The trial-specific reactivation results give a much more clear picture of the types of reactivation supporting their retroactive memory effects, and the extent of the specificity is quite compelling.

Similarly, the additional controls of the post-encoding analyses were quite strong. One point did emerge that was interesting, which was that there were negative correlations with neutral memory. These findings, albeit with some speculation, suggest that there is a prioritization of related emotional information rather than just an enhancement, meaning there may be a trade-off with neutral information. This would be very aligned with the circuits of interest and the role of noreadrenaline (see Clewett & Murty, 2019). While not completely necessary for this manuscript, additional discussion of this feature of the data that emerged from the new analyses could be included.

Reviewer #2:

The manuscript by Zhu et al. has improved with revision; I appreciate the addition of the more specific trial-level reactivation analysis which more directly tests the notion that reactivation is a mechanism supporting retroactive emotional memory enhancements. The authors have responded to the prior concerns thoroughly. New details were added regarding ROI definition, in response to prior comments. These new details currently limit my enthusiasm for the manuscript due to the lack of straightforward presentation of some of the analyses/results in the paper: how regions in Figures 4-6 were identified (all other results/analyses are clearly described; see below). Otherwise, the paper makes an important contribution to the field.

A primary concern is the way regions were isolated/identified in the analyses performed after the pattern similarity (those in Figures 4-6). I am not sure whether the FFA/LOC was defined once (i.e. those used in Figures 2-3) and then voxels within those regions were isolated showing connectivity effects for analyses in Figures 4-6, or whether separate regions were isolated across each analysis and are referred to with a common label (implying they are the same). This stems from the second paragraph of the ROI definition section (Methods) which I found to be unclear. As written, it is *implied* that FFA and LOC are re-defined in subsequent analyses "FFA and LOC were derived from a group contrast map of the aversive relative to neutral condition" and "significant clusters (i.e. LOC…) … were derived from the group-level multiple regression analysis on connectivity…". If the ROIs are re-defined in different analysis, it is misleading to use the same label to refer to them across analyses (which implies that they are the same regions or would at least be restricted to a common definition). Please both (1) clarify the approach because it is still not clear to me how the ROIs were defined for connectivity analysis (i.e. did you search for specific contrast within the original FFA/LOC definitions, or were these ROIs re-defined for each analysis within the broader anatomical mask from WFU pickatlas) and (2) the description of the ROIs/results in the main manuscript text should not be misleading as to how the analysis is conducted. If separate regions are isolated for different analyses, then this should be reflected in the labels used for these regions (they should not all have the same label which imply face and object processing regions if they are isolated by other means).

The ROI definition issue raises possible problems of non-independence and multiple comparisons corrections which are currently obfuscated given that the analysis steps are not spelled out. If the 'FFA' and 'LOC' regions shown in Figure 4 were defined from showing greater functional connectivity w/ the hippocampus during aversive vs. neutral trials (not how these regions are typically defined), then the results reported in Figure 4C are non-independent and inferential tests (ANOVAs) should not be performed on this data as it is circular. It is also stated that the gPPI was performed separately for initial encoding and emotional learning phases and clusters were isolated from each – presumably the clusters shown in Figure 4 were isolated from the emotional learning analysis since they show a strong effect in that phase? Please state which analysis they were defined from. The problem of non-independence seems to be remedied in Figure 6 since no statistics are reported and just brain regions are shown. Another possible issue is multiple comparisons corrections, especially *if the FFA/LOC definitions are not carried forward into subsequent analyses*. The original ROI definition does not include an explicit correction but uses a stringent threshold of P<.0001, 30 voxels (fine for ROI definition such that ROIs are interrogated in later analyses). But this thresholding procedure is referenced in subsequent analyses, so presumably all regions isolated in Figures 4-6 are using this criterion. Thus it needs to be specified how this criterion satisfies multiple comparison/family wise error correction / was chosen. Moreover, please clarify if the FFA/LOC regions in Figures 4-6 were also constrained anatomically by the wfu pickatlas regions and neurovault contrasts.

Reviewer #3:

In this version of the manuscript, Zhu and colleagues have revised their introduction and discussion to situate their experiment within the framework of sensory preconditioning, rather than behavioral tagging. I found the theoretical framework to be much better developed in this revision, and the results link more clearly to the existing conditioning and memory integration literature. Another major revision was the inclusion of a trial-specific reactivation analysis, in which they showed that trial-specific hippocampal patterns were reinstated during the emotional learning phase. This could provide a mechanism by which initially-neutral associations are strengthened through emotional learning. This is an interesting and potentially important result.

In reading the manuscript again, I was impressed with the robust behavioral findings, and I thought the pattern similarity analyses were largely convincing (see one comment below).

I remain concerned that there is too much emphasis placed on the results based on across-subject correlations, given the relatively small sample size (N=28). This includes the structural equation model as well as the results of the resting-state functional connectivity analyses. I am more convinced by the prediction analyses that follow up on results from the pattern similarity and task-related connectivity analyses. The resting-state analyses are particularly susceptible to the problems of underpowered correlations because they were computed across all voxels in the brain (see Marek et al. 2022 Nature for further discussion). In their response to the previous reviews, the authors described the across-subject correlation analyses as "exploratory" and complementary to the main lines of evidence. Yet this is not how they are presented in the paper itself, where the SEM and resting-state analyses are highlighted as key findings (e.g., see lines 558-563 in the results summary on p. 29). At minimum, more caution should be expressed throughout, with analyses clearly marked as exploratory.

For the structural equation model, the authors used a bootstrapping technique that may improve the ability to estimate direct and indirect effects even in small sample sizes. The model fit indices, however, seem to have been computed in the standard way. Given that the analysis may be underpowered to detect model misspecification, the authors should tone down their description of the model fit (both for the primary model as well as the alternative tested models).

In the pattern similarity analyses, reinstatement was observed at the onset of the voice but not in response to the face cue alone. Yet it appears that the voice onset occurred only 2s after the onset of the face cue, which is the equivalent of one TR. With that temporal resolution, it shouldn't be possible to reliably separate these two signals in time, and including them in the same model may lead to unstable parameter estimates. How have the authors addressed this?

There are a couple of places in the Results where non-significant results are described as significant, in cases when one ROI shows a significant effect but the other doesn't: the LOC interaction in line 295, the amygdala correlation in line 433. Conclusions should also be updated to refer only to those findings that are significant.

https://doi.org/10.7554/eLife.60190.sa1

Author response

Reviewer #1:

Zhu and colleagues present a manuscript detailing neural mechanisms driving associative memory enhancements for previously neutral information using an acquired equivelance paradigm. The authors show greater associative memory for face-word object pairs, when the face is later paired with an aversive event. Further, these memory enhancements were associated with increased interactions between the amygdala, hippocampus, relevant-sensory cortex, and higher-order associative cortex both during encoding and post-encoding rest. The question is quite timely, and the results provide a nice extension of rodent work. The analysis plan was well designed, and the results were highly interesting. However, there were some weaknesses in the methodological approach as well and the interpretation of the data that derail from the overall impact. These concerns are detailed below.

We appreciate the positive evaluation and insightful comments raised by the Reviewer. In response to the Reviewer’s comments, we have conducted several additional analyses (see below) to address the methodological issues and data interpretation, and have revised the manuscript accordingly. We feel that with our additional analyses and revisions, our findings and conclusions about emotion-charged retroactive memory enhancement for previously neutral event are now more clearly supported.

1. While the authors provide a great mechanistic understanding of retroactive memory effects, the framing around behavioral tagging and studies related to Dunsmoor et al., (2015) seem inaccurate. In the behavioral tagging literature, the focus is on retroactive memory effects for information that is un-related to the arousing events. By using a sensory pre-conditioning paradigm, the same stimuli are shown during both the pre-arousal phase and the arousal phase. The authors tend to conflate sensory preconditioning with behavioral tagging. This is especially relevant as the authors show immediate memory benefits which are more consistent with sensory pre-conditioning rather than behavioral tagging. With that in mind, I think the introduction needs to be significantly re-worked and more work needs to be done integrating the findings with human sensory pre-conditioning literature (i.e., Wimmer & Shohamy 2012).

We appreciate the Reviewer for thoughtful comments and suggestions. We agree that our observed retroactive memory effect is more consistent with sensory preconditioning protocols. We have now undertaken several steps to emphasize sensory preconditioning literature and avoid conflating that with behavioral tagging in our revised manuscript. First of all, we would like to clarify that we introduced behavioral tagging literature as one of potential neurobiological mechanisms underlying the retroactive memory enhancement. As we had already discussed in our original manuscript, the retroactive memory benefit in our study differed from the conventional behavioral tagging effects, by at least three following aspects: (1) we observed the trial-specific retroactive memory benefit with associative learning task in which overlapping face cues were presented during both initial and emotional learning phases, rather than a category-level effect with non-overlapping stimuli proposed in behavioral tagging literature; (2) we observed the retroactive memory benefit in the immediate test, which differs from the delayed benefit pertaining to behavioral tagging literature; (3) we observed the retroactive benefit selective for strongly encoded associations with high-vividness ratings (presumably strong memories; also see our response to point 2 below), which differs from the rescued effect for weak memories expected by behavioral tagging literature. Hence, we fully agree that our observed retroactive memory benefit is more consistent with sensory preconditioning paradigm, which also accommodates with memory integration models.

Second, according to the Reviewer’s suggestion, we have now rewritten the first three paragraphs of the Introduction section to emphasize the sensory preconditioning literature in our revised manuscript (Page 1-3). Specifically, in the first paragraph, we introduced the retroactive enhancement of emotional experience on related memory for previously mundane events in our integrated episodic memory system (Holmes et al., 2022; Shohamy and Daw, 2015; Wong et al., 2019), and raised an open question on its underlying neurocognitive mechanisms in humans. Then in the second paragraph, we focused on the emotion-charged retroactive enhancement for specific episodic memory, referred to in the text as trial-specific effect, which more closely fits our everyday memories but goes beyond explanations by conventional behavioral tagging models (Ballarini et al., 2009; Clewett et al., 2022; Dunsmoor et al., 2015; Takeuchi et al., 2016). We thus introduced the sensory preconditioning paradigm which allows us to investigate trial-specific effect by associative learning tasks, and also introduced previous studies of sensory preconditioning in animals and humans which provide evidence for trial-specific retroactive effects on associated events (Brogden, 1939; Kurth-Nelson et al., 2015; Li et al., 2008; Sadacca et al., 2018; Sharpe et al., 2017; Wimmer and Shohamy, 2012). We further raised an open question on the neurobiological mechanisms underlying such emotion-charged trial-specific effect. Moreover, in the third paragraph, we introduced memory allocation and integration mechanisms which have been proposed to accommodate sensory preconditioning (Holmes et al., 2021; Schlichting and Frankland, 2017; Schlichting and Preston, 2015; Shohamy and Daw, 2015; Shohamy and Wagner, 2008; Wong et al., 2019), to understand the potential neurocognitive processes (i.e., reactivation of hippocampal and stimulus-sensitive neocortical representations, as well as hippocampal–neocortical coordinated interactions) underlying the emotion-charged trial-specific retroactive memory benefit.

Third, we have substantially adapted the Discussion section of our revised manuscript (Page 14). We decided to mainly interpret our results in the context of sensory preconditioning literature and memory integration models (Arcediano et al., 2003; Holmes et al., 2021; Li et al., 2008; Schlichting and Preston, 2015; Shohamy and Daw, 2015; Shohamy and Wagner, 2008; Wimmer and Shohamy, 2012; Wong et al., 2019). We have also elaborated the above-mentioned three major differences between our behavioral data and previous findings reported in behavioral tagging literature. We further concluded that our findings of immediate, trial-specific effects for relatively strong neutral memories provide new insights into the possible mechanisms underlying emotion-charged retroactive memory benefits. It may extend the existing views of sensory preconditioning and memory integration models in the literature.

Thank the Reviewer again for raising this important point. The integrity of the Introduction section and data interpretation in the Discussion section has now been improved in our revised manuscript.

2. Related to the above point, I think two additional factors need to be addressed when thinking about interpreting the data through the lens of behavioral tagging versus sensory preconditioning. The first is that effects were occurring during an immediate memory test, as behavioral tagging is thought to support delayed effects. The second would be a moderating role for memory strength during phase 1, perhaps by using the vividness ratings. Behavioral tagging should produce larger effects on weak items (i.e., low vividness), while sensory preconditioning should produce larger effects on strong items (i.e., high vividness).

We appreciate the Reviewer for this thoughtful suggestion. For the first factor regarding to immediate versus delayed memory enhancement effects, we have now decided to interpret our findings of emotion-charged retroactive memory benefit mainly through a lens of sensory preconditioning literature (Arcediano et al., 2003; Holmes et al., 2021; Li et al., 2008; Shohamy and Daw, 2015; Wimmer and Shohamy, 2012). We have further clarified the major differences between the retroactive memory benefit observed in our current study and those reported in previous behavioral tagging studies (Ballarini et al., 2009; Braun et al., 2018; Dunsmoor et al., 2015). Besides the immediate effect (vs. delayed effect) pointed by the Reviewer, we also discussed other potential differences pertaining to trial-specific versus category-level effect, and selective effect on relatively strong memories for initial neutral events versus weak ones. We have now clarified this issue in the Discussion section of our revised manuscript (Page 14).

In light of the Reviewer’s comment on the second factor, we conducted separate paired t-tests for associative memory performance with low and high vividness ratings across three independent studies. In line with our originally reported results, we observed the reliable emotion-induced retroactive benefit only occurring on associations remembered with high confidence. These analyses on high-confidence memory performances (see Figure 1E-G) revealed significantly better memory in the aversive than neutral condition for high-vividness encoded associations (Study 1: t(29) = 2.15, p = 0.040, dav = 0.21; Study 2: t(27) = 2.16, p = 0.040, dav = 0.25; Study 3: t(27) = 2.21, p = 0.036, dav = 0.12), but not for low-vividness encoded associations (Study 1: t(29) = 0.27, p = 0.793; Study 2: t(27) = -0.58, p = 0.565; Study 3: t(27) = -0.40, p = 0.696). These results indicated a selectively emotion-induced memory enhancement only for initially high-vividness encoded neutral associations (potentially strong items).

Parallel analyses for those associations remembered with low confidence (see Figure 1—figure supplement 5), however, did not show consistent results across three studies. These analyses revealed a marginally significant emotion effect (i.e., aversive vs. neutral) for low-vividness encoded associations in Study 1 (t(29) = 1.97, p = 0.058), which was not replicated by Study 2 (t(27) = 0.27, p = 0.787) nor Study 3 (t(27) = -1.74, p = 0.094). No significant emotion effect for high-vividness encoded associations was observed in three studies (Study 1: t(29) = -0.98, p = 0.334; Study 2: t(27) = -0.15, p = 0.886; Study 3: t(27) = -1.10, p = 0.283). Given above inconsistent and insignificant results across three studies, the emotional effects for low-confidence remembered associations would not be discussed in the revised manuscript.

In our revised manuscript, we have noted above new results in the Results section (Page 6), and it now reads as follows: “To further investigate whether initial encoding strength of neutral associations (i.e., vividness rating during initial learning phase on a four-point scale: 4 = “Very vivid”, 1 = “Not vivid at all”) modulates our observed emotion-induced retroactive effect above, we sorted these associations remembered with high confidence into a high-vividness bin with 3 and 4 ratings, and a low-vividness bin with 1 and 2 ratings. Separate paired t-tests for the three independent studies revealed significantly better memory in the aversive (vs. neutral) condition, selectively for high-vividness encoded associations (Study 1: t(29) = 2.15, p = 0.040, dav = 0.21; Study 2: t(27) = 2.16, p = 0.040, dav = 0.25; Study 3: t(27) = 2.21, p = 0.036, dav = 0.12), but not for low-vividness encoded associations (Study 1: t(29) = 0.27, p = 0.793; Study 2: t(27) = -0.58, p = 0.565; Study 3: t(27) = -0.40, p = 0.696; Figure 1E-G). Additionally, parallel analyses were also conducted for those associations remembered with low confidence. However, we did not observe any reliable effect across three studies (Figure 1—figure supplement 5 with statistics). Altogether, these results indicate that our observed emotion-induced retroactive benefit for related memory only occurs on relatively strong-encoded associations with high-vividness rating.”

We have also discussed above results in the Discussion section (Page 14), and it now reads as follows: “Third, our observed retroactive benefit was selective for strongly encoded associations with high-vividness ratings (presumably strong memories), which contradicts with the rescued effect for weak memories expected by the behavioral tagging model (Dunsmoor et al., 2015; Holmes et al., 2021; Ritchey et al., 2016; Wong et al., 2019). Neutral associations with high-vividness ratings were, at least temporarily, encoded into memory during initial learning phase. Their memory traces could be subsequently reactivated by corresponding face cues, and further transformed into a more stable and long-lasting state by arousal during emotional learning phase. However, associations with low-vividness ratings might be encoded relatively weaker. Since no specific memory traces were formed for these low-vividness associations, they could not be reactivated nor enhanced later. It is thus conceivable that the emotion-charged trial-specific retroactive memory integration tends to occur on relatively strong associations.”

Since the behavioral tagging literature is less relevant to interpret our observed immediate and trial-specific effect on strongly encoded associations, we have now decided to avoid using the terms of “emotional tagging” throughout the revised manuscript. Instead, we used “emotional learning”, “emotional arousal” or “emotion-charged” when appropriate in the context. Given the observed selective effect on high-vividness encoded associations (i.e., relatively strong items), we have also avoided any claims on the transformation of initially weak memories into strong memories throughout the revised manuscript.

Thank the Reviewer again for prompting us to analyze our behavioral data regarding to vividness ratings. Outcomes from these new analyses further strengthen our conclusions of the emotion-charged retroactive memory benefit.

3. While I found the RSA results to be quite interesting, I had a few methodological concerns that made me question their validity. First, in general affective information can increase activation in many of the ROIs of interest in a non-specific way, including the FFA and LO. The authors should re-run their analyses controlling for univariate activation. Second, I think the RSA comparisons could be more specific and better controlled. Rather than collapsing across the entire condition, the RSA analysis would be more powerful run on an individual-trial level. Thus, the comparisons would be RSA between a pair of trials in phase 1 and 2, and then that trial in comparisons to all other trials within the same condition and all other trials in the other condition. This analysis would show whether a specific item is being reinstated or a more broad category representation.

The Reviewer raised very good points. According to the Reviewer’s suggestions, we have now conducted several new additional analyses as detailed below. First, we re-ran our RSA analyses by controlling the difference in univariate activation between aversive and neutral conditions. We implemented two steps to control the univariate activation differences for each ROI (i.e., hippocampus, LOC and FFA) and have now added several sentences to incorporate these steps into the Methods section of our revised manuscript. It reads as follows:

1) Page 21: “We eliminated the mean activation from each pattern by z-scoring across voxels of each ROI. Thus, the resulting mean-centered pattern has relative voxel amplitudes (i.e., voxel-level variability) preserved without any difference in the overall amplitudes, ensuring that the subsequent similarity results are fully attributable to the pattern itself (Coutanche, 2013; Tompary and Davachi, 2017).”

2) Page 22: “we conducted separate 2 (Emotion: aversive vs. neutral) by 2 (Measure: pair-specific vs. across-pair) repeated-measures ANCOVAs in each ROI, also with individual’s univariate activation differences (aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest to further mitigate the potential interference of overall activation from pattern results.”

By these two steps, we believe that our analyses are valid and state-of-the-art to reveal the emotion-induced effects on pattern similarity while controlling univariate activation differences.

Second, we re-ran our RSA analyses on individual-trial level and computed the following three measures of trial-level pattern similarity. As shown in Figure 2—figure supplement 3, multi-voxel activity pattern of each trial (i.e., face-voice pair) during emotional learning phase was separately correlated with: (1) its corresponding face-object pair during initial learning (pair-specific similarity), (2) different face-object pairs within the same aversive/neutral condition during initial learning (across-pair within-condition similarity), and (3) different face-object pairs from the different condition during initial learning (across-pair between-condition similarity).

For above three pattern similarity measures, we conducted separate 2 (Emotion: aversive vs. neutral) by 3 (Measure: pair-specific vs. across-pair within-condition vs. across-pair between-condition) repeated-measures ANCOVAs in each ROI (see Figure 2—figure supplement 4), with individual’s univariate activation differences (aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest. This analysis for hippocampal pattern similarity revealed a significant main effect of Emotion (F(1, 25) = 11.10, p = 0.003, partial η2 = 0.31) and an Emotion-by-Measure interaction (F(2, 50) = 3.72, p = 0.031, partial η2 = 0.13), but no main effect of Measure (F(2, 50) = 0.62, p = 0.541, partial η2 = 0.02). Post-hoc comparisons (by controlling univariate activation differences between aversive and neutral conditions) revealed a significantly higher pair-specific similarity in the aversive than neutral condition (F(1, 25) = 7.24, p = 0.013, partial η2 = 0.22), but no such Emotion effect in across-pair within-condition similarity or across-pair between-condition similarity (both F(1, 25) < 1.16, p > 0.290, partial η2 < 0.05). It also revealed that pair-specific similarity was significant higher than across-pair within-condition similarity (F(1, 25) = 4.43, p = 0.046, partial η2 = 0.15), and marginally significant higher than across-pair between-condition similarity (F(1, 25) = 3.96, p = 0.058, partial η2 = 0.14) in the aversive condition. No difference among these three similarity measures was observed in the neutral condition (all F(1, 25) < 1.00, p > 0.330, partial η2 < 0.04). Parallel analysis for LOC pattern similarity also revealed a significant main effect of Emotion (F(1, 25) = 8.45, p = 0.008, partial η2 = 0.25), but neither a main effect of Measure (F(2,50) = 0.16, p = 0.849, partial η2 = 0.01) nor an Emotion-by-Measure interaction (F(2, 50) = 1.87, p = 0.165, partial η2 = 0.07). Consistent with hippocampal similarity, post-hoc comparisons with covariates controlled revealed a significantly higher pair-specific LOC similarity in the aversive than neutral condition (F(1, 25) = 8.28, p = 0.008, partial η2 = 0.25), but no such Emotion effect in across-pair within- and between-condition similarity measures (both F(1, 25) < 2.91, p > 0.100, partial η2 < 0.11). The pair-specific LOC similarity was also marginally significant higher than across-pair between-condition similarity in the aversive condition (F(1, 25) = 3.42, p = 0.076, partial η2 = 0.12). However, parallel analysis for FFA pattern similarity revealed neither main effects of Emotion (F(1, 25) = 2.30, p = 0.142, partial η2 = 0.08) and Measure (F(2,50) = 0.53, p = 0.591, partial η2 = 0.02) nor their interaction effect (F(2, 50) = 0.06, p = 0.938, partial η2 = 0.003). These results indicated that emotional arousal increased pair-specific pattern similarity, rather than across-pair within- nor between-condition similarities, in the hippocampus and LOC. No effect was found in the FFA, because the visual input of face cues during emotional learning might cover potential reactivation pattern of initial face information.

Since there was no significant difference between across-pair within- and between-condition similarity measures (F(1, 25) < 1.30, p > 0.268, partial η2 < 0.05) and no Emotion effect (i.e., aversive vs. neutral) in each across-pair similarity measure (F(1, 25) < 2.91, p > 0.100, partial η2 < 0.11) for each ROI, we combined all across-pair similarities (i.e., averaging all correlations in both across-pair within- and between-condition) to quantify a generally category-level representation pattern. To better characterize the relationships between trial-specific reinstatement (pair-specific similarity) and category-level representation (across-pair similarity), we conducted separate 2 (Emotion: aversive vs. neutral) by 2 (Measure: pair-specific vs. across-pair) repeated-measures ANCOVAs in each ROI, also with individual’s univariate activation differences (aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest (see Figure 2B). Consistent with above results, these analyses revealed significant main effects of Emotion in the hippocampus (F(1, 25) = 9.48, p = 0.005, partial η2 = 0.28) and LOC (F(1, 25) = 9.34, p = 0.005, partial η2 = 0.27) as well as Emotion-by-Measure interaction effects in the hippocampus (F(1, 25) = 4.89, p = 0.036, partial η2 = 0.16) and LOC (F(1, 25) = 3.54, p = 0.072, partial η2 = 0.12), but no main effects of Measure (F(1, 25) < 0.60, p > 0.440, partial η2 < 0.03 in both ROIs). Post-hoc comparisons with covariates controlled revealed significant higher pair-specific similarity (both statistics were the same as above), and marginally significant higher across-pair similarity in the hippocampus (F(1, 25) = 4.16, p = 0.052, partial η2 = 0.14) and LOC (F(1, 25) = 3.37, p = 0.078, partial η2 = 0.12) in the aversive than neutral condition. It also revealed the (marginally) significant higher pair-specific than across-pair similarity in both the hippocampus (F(1, 25) = 4.42, p = 0.046, partial η2 = 0.15) and LOC (F(1, 25) = 2.96, p = 0.097, partial η2 = 0.11) in the aversive condition, but not in the neutral condition (F(1, 25) < 2.20, p > 0.150, partial η2 < 0.08 in both ROIs). Parallel analysis for FFA pattern similarity revealed neither main effect nor interaction effect (all F(1, 25) < 1.59, p > 0.219, partial η2 < 0.06). These results more directly indicated that emotional arousal promoted greater trial-specific reinstatement relative to category-level representation in the hippocampus and LOC, supporting our observed trial-specific retroactive memory enhancement.

Author response image 1
Post-encoding hippocampal connectivity changes in relation to memory difference (i.e., aversive vs. neutral).

(A) Significant cluster in the lateral occipital cortex (LOC), showing its greater connectivity with the hippocampus at Rest 2 (vs. Rest 1) in positive relation to the memory difference. (B) Significant clusters in the right anterior prefrontal cortex (aPFC), the bilateral inferior parietal lobule (IPL) extending into angular gyrus and the right posterior cingulate cortex (PCC), showing their greater connectivity with the hippocampus at Rest 3 (vs. Rest 2) in positive relation to the memory difference. Notes: Color bars represent T values; two-tailed tests.

Altogether, we hope the Reviewer is now convinced that results from above new analyses suggest an emotion-induced enhancement in trial-specific reinstatement of initially encoded memories, rather than in broad category representation, in the hippocampus and LOC. However, given the overlap in faces shown during both initial and emotional learning phases, FFA pattern similarity did not reflect a pure reinstatement (or reactivation) of face information, and thus did not show the trial-specific emotional effect. The marginally significant emotional effects for across-pair similarity (i.e., category-level representation) in three ROIs might be due to that the stimuli associated with screams generally attracted more attention to the aversive compared to neutral condition, rather than pattern reinstatement (or reactivation). Hence, our observed retroactive memory benefit is most likely resulted from an emotion-enhanced trial-specific reinstatement of the hippocampal and LOC activity patterns, rather than a broad category representation due to arousal-based attentional priority in the aversive (vs. neutral) condition. We have now added these results into the Results (Page 7; Figure 2A and B) and the Supplemental Materials (Figure 2—figure supplement 3 and Figure 2—figure supplement 4). We also updated the Discussion (Page 14-15) and Methods (Page 22) sections accordingly of our revised manuscript.

We would like to thank the Reviewer again for raising above methodological concerns and suggestions, which prompt us to conduct a set of additional trial-level pattern similarity analyses. Outcomes from these analyses are very interesting, and provide novel trial-specific evidence to complement our original findings at the condition level.

4. There is a growing body of work characterizing post-encoding mechanisms contributing to memory benefits, in particular emotional memory benefits, that should be included in the discussion (see DeVoogd, Tambini, Murty, Hermans).

We thank the Reviewer for this specific suggestion. We have now added several sentences in the Discussion section to better discuss possible post-encoding mechanisms that contribute to the emotional memory benefits in our study. The references suggested by the Reviewer have been also cited appropriately. It now reads as follows (Page 16): “Besides, neural circuits activated by emotional learning exhibit persistent activity and alter a series of systems-level interactions during post-emotional-learning rest (i.e., hippocampal connectivity with aPFC, mPFC, PCC and IPL) (de Voogd et al., 2016; Hermans et al., 2017; Murty et al., 2017). Thus, our observed offline hippocampal-neocortical connectivity changes may contribute to emotion-charged retroactive memory benefit, likely through functional reorganization mechanisms of memory-related brain systems at both pre- and post-emotional-learning phases.” and “In line with emotion-charged retroactive memory benefit and increased trial-specific reactivation for associated events, our observed hippocampal connectivity changes during emotional learning and post-learning rests most likely reflect relational and/or contextual processes underlying memory integration, but not due to an indiscriminate generalization or persistence of emotional arousal during learning and post-learning rest (Hermans et al., 2017; Tambini and Davachi, 2013; Tambini et al., 2010). ”

5. The post-encoding analyses need to have additional controls. Ideally, the authors would run an interaction analysis to characterize regions in which post-encoding coupling predicts Emotional but not Neutral memory. Further, control analyses in sensory regions that are not directly related to the stimuli (such as PPA) would be beneficial.

We appreciate the Reviewer for this good point. We have now undertaken the following steps to investigate the potential interaction effects on how post-encoding coupling changes predicted aversive but not neutral memory. We first computed the difference maps of hippocampal connectivity during post-initial-learning rest (relative to before: Rest 2 minus Rest 1), as well as during post-emotional-learning rest (relative to before: Rest 3 minus Rest 2). Thereafter, we submitted these difference connectivity maps (i.e., Rest 2 vs. 1, and Rest 3 vs. 2) into the second-level multiple regression models, with memory accuracies in aversive and neutral conditions as two separate covariates of interest. We then conducted a contrast to test the difference in regression coefficients between aversive and neutral conditions – that is the interaction effects of post-encoding hippocampal connectivity in relation to memory in the aversive versus neutral condition. This interaction analysis identified significant clusters in the LOC (Rest 2 vs. Rest 1), and the inferior parietal lobule (IPL), the posterior cingulate cortex (PCC), the anterior prefrontal cortex (aPFC) and the medial prefrontal cortex (mPFC) (Rest 3 vs. Rest 2) (see Figure 4B and C, Figure 4—figure supplement 1 and Table supplement 4). Specifically, we found that post-encoding hippocampal connectivity with stimulus-relevant LOC at post-initial-learning rest positively predicted associative memory in the aversive (controlling for neutral memory, partial r = 0.52, p = 0.007) but not neutral (controlling for aversive memory, partial r = -0.50, p = 0.010) condition (see Figure 4—figure supplement 1A). We also found that post-encoding hippocampal connectivity with transmodal prefrontal-parietal regions (i.e., IPL, PCC, aPFC and mPFC) at post-emotional-learning rest positively predicted memory in the aversive (controlling for neutral memory, IPL: partial r = 0.61, p = 0.001; PCC: partial r = 0.55, p = 0.003; aPFC: partial r = 0.56, p = 0.003; mPFC: partial r = 0.54, p = 0.004) but not neutral (controlling for aversive memory, IPL: partial r = -0.61, p = 0.001; PCC: partial r = -0.54, p = 0.004; aPFC: partial r = -0.56, p = 0.003; mPFC: partial r = -0.57, p = 0.002) condition (see Figure 4—figure supplement 1B). Besides the regions reported in our original manuscript with memory difference (i.e., aversive vs. neutral) as a single covariate of interest (see Author response image 2), this interaction analysis revealed another significant cluster in mPFC, a core default network structure. Together, these new interaction results replicated and complemented our original findings. It suggests a shift of post-encoding hippocampal connectivity from stimulus-relevant neocortex to transmodal prefrontal-parietal areas (i.e., the default network) at rest states, which could contribute to our observed emotion-charged retroactive memory enhancement.

Author response image 2
Hippocampal connectivity changes from baseline rest (Rest 1) to post-initial-learning rest (Rest 2) in relation with general/non-arousal memory performance.

(A) Significant clusters show their greater connectivity with the hippocampus at Rest 2 relative to Rest 1, with average memory across aversive and neutral conditions as the covariate of interest. (B) Significant clusters show their greater connectivity with the hippocampus at Rest 2 relative to Rest 1, with memory in neutral condition as the covariate of interest. Notes: Color bars represent T values; L, left; R, right.

Author response table 1
Post-encoding PPA connectivity changes in negative relation to memory in the aversive but not neutral condition.
Brain RegionsHemisphereT valuesMNI Coordinates
XYZ
Rest 2 vs. Rest 1
AngularR-4.7444-5840
PrecuneusR-7.1210-5228
Rest 3 vs. Rest 2
Superior frontal gyrusL-4.93-20-474
Inferior frontal gyrusL-5.38-363014
Inferior Parietal LobuleL-4.61-40-5058
Supramarginal gyrusL-4.83-52-3028
Precentral gyrusL-8.48-5006
L-4.62-34-262
Lingual gyrusL-5.17-12-90-8

With respect to additional control analyses, it is worth noting that we did not find any significant cluster in other sensory regions including the parahippocampal place area (PPA) in our post-encoding hippocampal connectivity analyses. Thus, we assume that the Reviewer suggested conducting parallel post-encoding connectivity analyses with PPA as a seed of interest. We therefore defined the posterior parahippocampal gyrus with the WFU PickAtlas toolbox as a PPA mask (Epstein et al., 1999; Epstein and Ward, 2010). However, the posterior parahippocampal area is not only a key sensory region processing visual scenes, but also involved in the encoding of face-object associations into an integrated representation (Qin et al., 2007). To avoid potential interference, we further restricted the PPA seed by removing its overlapping area with two activation contrast maps of encoding during initial and emotional learning as relative to fixation (i.e., all encoding trials vs. fixation during each learning phase), by a less stringent height threshold of p < 0.01 and an extent threshold of p < 0.05 corrected for multiple comparisons (see Figure 4D). Thereafter, we computed PPA-seeded functional connectivity maps for Rest 1, 2, and 3. These analyses revealed no any reliable clusters with positive effects survived for post-initial-learning (i.e., Rest 2 vs. Rest 1) and post-emotional-learning (i.e., Rest 3 vs. Rest 2) PPA connectivity, in relation to the above-mentioned interaction effect between aversive and neutral conditions. But there were several clusters with significant negative effects, which is out of scope of our study (see Author response table 1). Thus, we did not observe any reliable effect for PPA connectivity with other regions, indicating that our observed offline reorganization of hippocampal connectivity appears only for regions or connectivity pathways relevant to stimuli and tasks used in this study.

Notes: Regions were derived from the multiple regression analyses on post-encoding PPA-seeded functional connectivity with memory accuracies in aversive and neutral conditions as two separate covariates of interest. Clusters, significant at a height threshold of p < 0.001 and an extent threshold of p < 0.05 corrected for multiple comparisons, are reported with local maximum T statistic in Montreal Neurological Institute (MNI) space. L, left; R, right.

In sum, outcomes from our additional multiple regression analyses provide further evidence to strengthen our claim on a shift of post-encoding hippocampal connectivity from stimulus-relevant neocortex to transmodal prefrontal-parietal areas during offline rests before and after emotional learning. We have now updated the Results ( Page 13; Figure 4) and Methods (Page 23-24) sections with these new analyses in our revised manuscript. The other corresponding tables and graphs were included in the Supplemental Materials (Table supplement 4 and Figure 4—figure supplement 1). Additionally, the control PPA region was not involved in this rapid hippocampal-neocortical functional reorganization. We have now added several sentences to incorporate these additional control results into the Results section of our revised manuscript. It now reads as follows ( Page 13): “Additionally, we conducted parallel control analyses with the parahippocampal place area (PPA) as a seed that is a sensory region but not related to the stimuli used in our study. These control analyses revealed no reliable clusters survived for PPA connectivity during post-learning rests in relation with emotion-charged retroactive memory benefit (Figure 4D). It indicates that our observed offline hippocampal-neocortical functional reorganization is a special process involving with stimuli- and memory-related regions (i.e., LOC, IPL, PCC, aPFC and mPFC), but not with irrelevant brain regions (i.e., PPA).”

We hope that the Reviewer is now convinced by outcomes from our additional analyses for interaction effects on post-encoding hippocampal connectivity between aversive and neutral conditions.

Reviewer #2:

The manuscript by Zhu and colleagues examines the retroactive influence of emotional 'tags' on associative memory for tagged items, providing new behavioral evidence for a retroactive memory benefit associated with emotional arousal. This is replicated across a few behavioral cohorts and is importantly distinct from prior demonstrations that emotion can enhance item memory for material that is conceptually related to 'tagged' information. fMRI is used to examine possible neural mechanisms for the retroactive benefit: greater reactivation of initial learning patterns is present for the emotionally tagged condition, and hippocampal interactions are enhanced in this condition as well. There is also evidence for post-tagging resting hippocampal interactions that predict the emotional-tagging memory benefit, suggesting that both mechanisms at the time of 'tagging', and subsequent mechanisms may mediate this behavioral effect. I think the main results in the manuscript are sound and advance the literature.

We thank the Reviewer for the enthusiasm and positive evaluation of our manuscript. We are encouraged by the Reviewer’s commendation that “our main results in the manuscript are sound and advance the literature”. We also appreciate the thoughtful and constructive comments that improved our manuscript.

I think there a few points that need to be considered, however.

Main points:

1. The reinstatement effect (greater for emotional vs. neutral trials) is a compelling demonstration that is consistent with the retroactive memory benefit. A couple of points to consider regarding this analysis:

a. The current approach creates an average pattern for 'aversive' and 'neutral' stimulus pairs, both during tagging and initial learning. Thus, greater reinstatement for the aversive condition likely reflects category-level reinstatement of face/object information present during learning, rather than the specific reinstatement of a particular pair (which is implied in the discussion, e.g. "stimulating reactivation of overlapping memory traces" on line 414). Did the authors also examine evidence for pair-specific reinstatement (i.e. greater similarity between the tagging of Face A and encoding trial of Face A, vs. the tagging of Face A and encoding of Face B)? Evidence for trial-specific reinstatement would provide a tighter mechanistic link between reactivation and enhanced associative memory associated with emotional tagging.

b. At face value, the relationship between reinstatement evidence and associative memory (in Figure 2D) helps to link reinstatement with subsequent memory. However, given that the relationship is not specific (present for both emotional and neutral conditions) it makes me wonder whether there are alternative explanations beyond reactivation explaining better memory. Since reinstatement is a simple relationship between across-trial encoding and tagging phases, it is possible that participants with less reliable or consistent activity patterns during either the encoding or tagging phase alone (due to fluctuations in attention or other factors) may have worse memory, which may drive the correlation. This can be addressed by (1): showing that the similarity within encoding, i.e. across trials or conditions is not predictive of memory, (2): the same for the tagging phase, and/or (3): by examining evidence for pair-specific reinstatement as described above, which is a more specific measure.

We appreciate the Reviewer for these thoughtful comments and suggestions. To examine (a) evidence for pair-specific reinstatement, we conducted additional analyses for trial-by-trial neural activation pattern similarity. This point is similar to the Reviewer #1’s point 3 (please also see our response to point 3 above). Briefly, we first computed the pair-specific multi-voxel pattern similarity between initial learning and emotional learning phases for each specific pair (i.e., correlation between the arousal trial pattern of Face A and encoding trial pattern of Face A), and across-pair similarity among different pairs (i.e., averaging all correlations between the arousal trial pattern of Face A and encoding trial patterns of Face B, C, D…) (see Figure 2—figure supplement 3). To examine whether there is reliable evidence for emotion-charged pair-specific reinstatement, we then conducted separate 2 (Emotion: aversive vs. neutral) by 2 (Measure: pair-specific vs. across-pair) repeated-measures ANCOVAs in each ROI, with individual’s univariate activation differences (aversive vs. neutral) in both initial and emotional learning phases as covariates of no interest (see Figure 2B). These analyses revealed (marginally) significant Emotion-by-Measure interaction effects in the hippocampus and LOC (see our response to point 3 for statistics). Post-hoc comparisons (with covariates controlled) revealed marginally significant greater pair-specific than across-pair similarity in both the hippocampus and LOC in the aversive condition, but not in the neutral condition (see our response to Q1-3 for statistics). It also revealed the significant greater pair-specific similarity in the aversive (vs. neutral) condition in the hippocampus and LOC (see our response to point 3 for statistics). No significant pair-specific effect showed in the FFA which was interfered with the visual input of face and did not reflect pure reactivation during emotional learning. Altogether, these results provided prominent trial-specific evidence to strengthen our original claim that the observed retroactive memory benefit was most likely resulted from emotional arousal stimulating reactivation of overlapping memory trace for each specific pair in the hippocampus and LOC.

Moreover, we conducted machine-learning based prediction analyses to examine the relationships between pair-specific/across-pair similarity and associative memory performance (see Figure 2C and Figure 2—figure supplement 5). These analyses revealed that hippocampal pair-specific similarity positively predicted memory in the aversive (r(predicted, observed) = 0.46, p = 0.008) but not neutral condition (r(predicted, observed) = -0.09, p = 0.480). Further Steiger’s test (Steiger, 1980) revealed a significant difference in correlation coefficients of hippocampal pair-specific similarity between aversive and neutral conditions (z = 2.54, p = 0.011). Hippocampal across-pair similarity positively predicted memory in both aversive (r(predicted, observed) = 0.43, p = 0.014) and neutral (r(predicted, observed) = 0.50, p = 0.001) conditions, but with no significant difference in correlation coefficients between two conditions (z = -1.09, p = 0.275). The prediction effects of hippocampal pair-specific relative to across-pair similarity on memory were non-significant in both conditions (both r(predicted, observed) < 0.10, p > 0.201). These results indicated that both trial-level reinstatement (i.e., pair-specific similarity) and category-level representation (i.e., across-pair similarity) could explain better memory. The category-level representation had a general predication effect for memory regardless of emotion arousal. However, the trial-specific reinstatement showed an emotion-bias predictive effect for memory in the aversive condition.

To further address (b) whether there are alternative explanations beyond reactivation explaining better memory, we conducted a set of new trial-by-trial pattern similarity analyses. Besides pair-specific and across-pair similarity between two phases outlined above, we also computed two other similarity measures across trials: (1) within-encoding similarity (i.e., averaging all correlations among patterns of face-object pairs within initial learning phase), and (2) within-arousal similarity (i.e., averaging all correlations among patterns of face-voice pairs within emotional learning phase) (see Figure 2—figure supplement 3). Thereafter, we conducted machine-learning prediction analyses of these two similarity measures with associative memory performance. As shown in Figure 2—figure supplement 6, we did not find any reliable relation of within-encoding similarity (aversive: r(predicted, observed) = -0.20, p = 0.676; neutral: r(predicted, observed) = -0.11, p = 0.543) and within-arousal similarity (aversive: r(predicted, observed) = -0.28, p = 0.960; neutral: r(predicted, observed) = -0.28, p = 0.960) with memory in both conditions. These results indicated that the reliability or consistency of activity pattern within each phase did not account for our observed emotion-charged retroactive memory benefit.

To summarize, three major findings could be drawn from our above new analyses. First, the emotional learning promoted greater trial-specific reactivation/reinstatement in the aversive than neutral condition in the hippocampus and LOC. Second, such pair-specific reinstatement was predictive of better associative memory in the aversive condition, but not evident in the neutral condition, indicating the specificity of emotional modulation effect. The category-level representation, however, generally predicted better memory regardless of aversive or neutral condition. Third, the pattern reliability or consistency during either initial learning or emotional leaning phase could not explain memory benefit in our study. Altogether, the emotion-charged retroactive memory benefit found in our study is more likely resulted from an emotion-induced increase in trial-specific reinstatement of initial encoding activity, but neither broad category-level representation nor reliable/consistent activity pattern within each phase. We have now added these new results into the Results (Page 7-9; Figure 2C), and have also updated the Discussion (Page 14-15) and Methods (Page 22) sections accordingly of our revised manuscript. The other corresponding graphs have been included in the Supplemental Materials (Figure 2—figure supplement 5 and Figure 2—figure supplement 6).

2. The differences in hippocampal interactions based on emotional tagging (Figure 3D,E) reveal greater coupling with the amygdala and FFA/LOC during emotional vs. neutral 'tagging', consistent with the retroactive emotional memory enhancement. The further analyses in Figure 3F,G indicate, however, that these differences are not specifically linked with the emotion-related memory enhancement and instead show main effects of memory and emotion (not an interaction). Despite this lack of differences due to the emotional memory enhancement (across trials) the authors examine whether individual differences in these measures are related to emotionally-tagged memory.

a. Please provide logic linking these analyses – why was it expected that these measures would predict emotionally-tagged memory across subjects when trial-level effects relating to emotional memory (specifically) were not found?

Indeed, we only observed the main Memory and Emotion effects for hippocampal connectivity depicted in Figure 3F and G in our original manuscript (now Figure 3E-G in the revised manuscript), but not specifically linked with the emotion-related memory enhancement (no interaction effect). We further conducted machine-learning prediction analysis to investigate the relationships between hippocampal connectivity and emotion-specific memory enhancement across subjects based on the following three aspects. First, evidence from many previous studies introduced in our original manuscript has converged onto linking emotion-modulated hippocampal coupling to subsequent memory benefits for emotional (compared to neutral) condition, suggesting the potential specificity of emotional modulation (de Voogd et al., 2017; Richardson et al., 2004). Second, we found the emotional specificity in reactivation/reinstatement analyses through significant interaction effect and correlation difference between aversive and neutral conditions (see Figure 2B and C). The hippocampal connectivity has been well proposed to coordinate with reactivation together to promote memory integration (Schlichting and Preston, 2014; Sutherland and McNaughton, 2000). Thus, we assume that hippocampal connectivity might also show pontential emotional specificity in some aspect. Third, it is not surprising that we found significant correlation difference but not interaction effect in hippocampal connectivity analyses, due to the different principles of these two statistical approaches. The interaction effect in ANOVA, based on group mean and variance, emphasizes different effects on connectivity itself between conditions (Nieuwenhuis et al., 2011). No significant interaction effect found in our study indicated that hippocampal connectivity for high-confidence remembered associations compared with forgotten ones was not significantly greater in the aversive than neutral condition. However, the prediction analysis catches up the linear covariation of connectivity and memory performance (Aggarwal and Ranganathan, 2016). Our results of significant correlation difference indicate that hippocampal connectivity (i.e., remembered with high confidence relative to forgotten) could better predict memory performance in the aversive than neutral condition (see our response to 2b below). In other words, emotion-charged hippocampal connectivity showed more direct and tight relationship with memory performance than that in neutral situation.

Based on above theoretical motivation, empirical observations as well as statistical principles, we have clarified the logic linking the ANOVAs and further prediction analyses on hippocampal connectivity more clearly in the revised manuscript. It now reads as follows:

Page 9: “Given the modulatory effects of emotion for hippocampal connectivity reported in previous studies (de Voogd et al., 2017; Richardson et al., 2004) and emotion-charged neural reactivation in relation to associative memory obeserved in our study, we assumed that hippocampal connectivity, which works in concert with reactivation to promote memory integration (Schlichting and Preston, 2014; Sutherland and McNaughton, 2000), might also show emotion-specific relationship with associative memory. Although we did not find reliable interaction effect in above ANOVA analyses, we employed machine learning-based prediction analysis approach to investigate whether greater hippocampal coupling during emotional learning could predict better memory and whether this relationship would show emotional specificity.”

We would like to thank the Reviewer again for prompting us to clarify this point.

b. Please also show the correlations that are described in the text (lines 327-335). It is a bit surprising to see the come up as boxes in the SEM analysis (Figure 3H) although they are not shown individually first. Do the correlations between memory and hippocampal connectivity (w/ amygdala and FFA/LOC) significantly differ between the emotionally-tagged and neutral conditions? The correlations are significant for the emotional condition, but not the neutral condition, implying specificity although this is not shown/tested. Or, the analysis can be done with the difference in memory for emotional vs. neutral conditions to gain this specificity, which was used for the rest analyses in Figure 4.

We thank the Reviewer for this suggestion. We have now conducted connectivity analyses in the FFA and LOC separately, instead of combining them as an overall neocortical region in our original manuscript, to elaborate our observed mechanism with regional specificity. This also was suggested by the Reviewer 3’s point 4. The correlations are shown in Figure 3H-J and Figure 3—figure supplement 2 . We then conducted Steiger’s tests to examine the difference between correlations in aversive and neutral conditions in each ROI.

Author response table 2
Hippocampal connectivity changes at post-initial-learning rest (Rest 2 vs.1).
Brain RegionsHemisphereT valuesMNI Coordinates
XYZ
Related to average memory
Superior temporal gyrusR3.69540-6
Superior temporal poleR3.995416-20
InsulaL-3.62-381410
CuneusR-3.8420-6624
Supramarginal gyrusR-3.4658-3236
Postcentral gyrusL-3.65-54-822
R-3.7664020
Related to memory in the neutral condition
Superior temporal gyrusR3.87540-6
ThalamusR3.5620-166
Postcentral gyrusR-3.5764020
CuneusR-3.5720-6624
Supramarginal gyrusR-3.3658-3236

To emphasize our main goal of the prominent emotional effects on hippocampal connectivity in the aversive condition, we have now added the aversive correlation graphs in the Results section of our revised manuscript (Figure 3H-J) and included the corresponding neutral graphs in the Supplemental Materials (Figure 3—figure supplement 2). If the Reviewer still prefers to add all correlation graphs including neutral ones in our revised manuscript, we are also happy to do that.

We have also updated the Results section of our revised manuscript (Page 9-11), and it now reads as follows: “Interestingly, we found that greater hippocampal connectivity with the amygdala, FFA and LOC (i.e., remembered with high confidence relative to forgotten) were predictive of better associative memory (i.e., remembered with high confidence) in the aversive condition (amygdala: r(predicted, observed) = 0.24, p = 0.076; FFA: r(predicted, observed) = 0.57, p < 0.001; LOC: r(predicted, observed) = 0.65, p < 0.001; Figure 3H-J), but not in the neutral condition (all r(predicted, observed) < 0.15, p > 0.160; Figure 3—figure supplement 2). Further Steiger’s tests revealed significant differences in correlation coefficients between two conditions for hippocampal coupling with FFA (z = 2.13, p = 0.033) and LOC (z = 2.15, p = 0.032), and marginally significant difference for hippocampal-amygdala coupling (z = 1.79, p = 0.073). These results indicate that emotion-charged hippocampal connectivity with the amygdala and stimulus-sensitive neocortical regions positively predicts associative memory for initial neutral events in the aversive but not neutral condition, also implying an emotional specificity effect.”

c. Lastly, logic should also be provided for constructing the SEM in the specific manner it was setup (hippocampal-cortical connectivity serving as the mediating variable between hipp-amygdala connectivity and memory).

Consistent with above correlation analyses, we have now constructed the SEM with hippocampal-FFA/LOC connectivity as separate mediators to account for the positive relationship between hippocampal-amygdala connectivity and associative memory (see Figure 3K).

This SEM was constructed based on the following considerations. First, as we introduced in our original manuscript, emotion-induced memory enhancement is most likely based on the modulatory role of the amygdala on hippocampal communication with the neocortex (Hamann, 2001; LaBar and Cabeza, 2006). This modulatory role may support more efficient information transmission and communication between the hippocampus and related neocortical regions (Alvarez et al., 2008; Hermans et al., 2017; Phelps and LeDoux, 2005), and thereby further lead to emotion-charged memory enhancement. Thus, we theoretically assumed that hippocampal-amygdala connectivity predicted memory most likely through hippocampal-neocortical dialogue. Second, the correlation analyses revealed that memory in the aversive condition was not only marginally correlated with hippocampal-amygdala connectivity (see Figure 3H), but also highly correlated with hippocampal-FFA/LOC connectivity (see Figure 3I and J). Therefore, we assumed the potential mediatory pathways among these variables, that hippocampal-amygdala connectivity might indirectly account for emotion-charged memory performance through the mediation of hippocampal-FFA/LOC connectivity. Third, besides above theoretical motivation as well as empirical observations, we conducted additional control analyses to examine alternative mediation and modulation models (see Figure 3—figure supplement 3) using the procedures similar to previous studies (Burghy et al., 2012; Jiang et al., 2020; Zhu et al., 2019). We observed no reliable mediating or modulating effect in these alternative models, practically indicating that our data did not support other models.

We have now clarified the above logic for the SEM construction and updated the Results section of our revised manuscript. We have also included the alternative models in the Supplemental Materials (Figure 3—figure supplement 3). It now reads as follows:

Page 11: “The modulatory role of the amygdala on hippocampal dialogue with the neocortex is recognized to promote more efficient information transmission and communication between the hippocampus and related neocortical regions (Alvarez et al., 2008; Hermans et al., 2017; Phelps & LeDoux, 2005), which could further lead to emotion-charged memory enhancement. In addition, according to our above empirical observations, memory performance in the aversive condition was not only marginally correlated with hippocampal-amygdala connectivity, but also highly correlated with hippocampal-FFA/LOC connectivity. Therefore, we assumed the potential mediatory pathway among these variables, that is, hippocampal-amygdala connectivity would indirectly account for emotion-charged memory performance through the mediation of hippocampal-FFA/LOC connectivity. Based on above theoretical motivation as well as empirical observations, we further implemented structural equation modeling (SEM) to investigate how the hippocampus, amygdala and neocortical systems during emotional learning work in concert with each other to support emotion-charged retroactive memory enhancement.

Specifically, we constructed a mediation model with hippocampal-amygdala connectivity (i.e., remembered with high confidence relative to forgotten) as input variable, hippocampal-FFA and -LOC connectivity pathways as two separate mediators and individual’s associative memory performance (i.e., remembered with high confidence) as outcome variable in the aversive condition (Figure 3K). The SEM analysis revealed a good model fit for our data (Chi2 = 1.88, p > 0.050; RMSEA = 0.00; SRMR = 0.035; CFI = 1.00). The two mediating effects on the positive relationship between hippocampal-amygdala connectivity and memory outcome were both significant (hippocampal couplings with FFA and LOC: Indirect Est = 0.19, p = 0.010, 95% CI = [0.044, 0.328], and hippocampal–LOC coupling: Indirect Est = 0.20, p = 0.016, 95% CI = [0.036, 0.360]). That is, hippocampal-amygdala connectivity affected hippocampal-LOC connectivity through a partial mediating effect of hippocampal-FFA connectivity, which could ultimately account for emotion-charged associative memory performance in the aversive condition. Notably, we also conducted additional control analyses for a set of alternative mediation and modulation models in the aversive condition (Figure 3—figure supplement 3). These analyses, however, did not reveal any reliable mediating or modulating effect, indicating that our data did not support such alternative models. Since there was no significant relationship between hippocampal connectivity and associative memory in the neutral condition, we did not conduct SEMs in this condition. Altogether, these results indicate that increased hippocampal-amygdala coupling indirectly accounts for emotion-charged associative memory, mediating by hippocampal-neocortical couplings during emotional learning.”

We have further added several sentences to discuss above results in the Discussion section ( Page 15-16), and it now reads as follows: “This mediation effect suggests that emotional arousal directly paired with face cues could induce greater hippocampal-amygdala connectivity acting on hippocampal-FFA connectivity during emotional learning, which then stimulated the cued trial-specific reactivation in the hippocampus and LOC through increasing hippocampal-LOC interaction, and ultimately contributed to retroactive memory benefit for related events.”

3. The primary rest analysis – which examines changes in hippocampal connectivity (rest-3 minus rest-2) that are related to individual differences in emotionally-tagged memory enhancement – is clear and well-motivated. However, the logic underlying the first analysis, which relates changes in connectivity from rest-1 to rest-2 (baseline to post-initial-learning rest) to the emotionally-tagged memory enhancement, is not clear. Given that emotional tagging occurs after rest-2, there should be no meaningful signature associated with biased consolidation of this material prior to this time point. I understand that the authors are trying to show how emotional tagging, per se, alters post-encoding consolidation signatures during rest above and beyond those that are present after initial learning alone. I infer that the goal is to compare the post-tagging rest to an analysis that illustrates simple relationships between immediate post-encoding connectivity and later memory (not related to tagging). To achieve this goal with the current design, I would suggest a more straightforward analysis of examining changes in hippocampal connectivity from rest-1 to rest-2 that are related to individual differences in associative memory that are not related to tagging, such as the average across neutral and emotionally-tagged stims (or perhaps neutral stimuli that are not emotionally tagged). Average associative memory would likely be the best measure, considering that emotional tagging may enhance memory for related material at the cost of impaired memory for non-tagged material. There is clear logic in this analysis, in that it would reveal initial post-encoding resting connectivity that is related to memory and NOT the emotional tagging procedure. The contrast of this kind of analysis with the rest-3 minus rest-2 analysis would then be clear. Note that the main analysis (examining changes from rest-2 to rest-3) on its own somewhat controls for initial post-encoding activity (captured in rest-2), so perhaps this other analysis is not needed (although the claims made in the Discussion would have to be altered).

We appreciate the Reviewer for raising this thoughtful comment and suggestion on how to characterize post-encoding hippocampal connectivity changes from rest-1 to rest-2 linked to subsequent memory performance. First of all, we would like to clarify the logic for our analyses of post-encoding hippocampal connectivity changes. As the Reviewer pointed out, our goal is indeed to compare hippocampal connectivity changes during post-emotional-learning rest with that during post-initial-learning rest in order to predict the same subsequent memory outcome – that is, the emotion-charged retroactive memory enhancement (i.e., interaction effects for post-encoding hippocampal connectivity changes between aversive and neutral conditions). This strategy allows us to test whether neural engagement prior to emotional learning contributes to the later emotion-charged memory benefits, implicated in memory allocation and integration models introduced in our original manuscript. These models propose that a preparatory state with relevant neuronal excitability may actively affect the way of how newly acquired memories are allocated and integrated into existing neural networks (Park and Rugg, 2010; Schlichting and Frankland, 2017; Yoo et al., 2012). In addition, from a perspective of statistical comparisons, this analytic strategy allows us to directly compare post-encoding connectivity changes as a function of before and after emotional learning, while holding the dependent variable same. We have clarified the logic of our above analytic strategy and also adjusted the discussion in our revised manuscript. It now reads as follows:

Page 12 in Results: “The offline neurocognitive processes along with hippocampal-neocortical functional reorganization are thought to support episodic memory (Liu et al., 2022; Tambini et al., 2010). Considering the potential influences of memory-related brain network configurations and representations both prior to and after emotional learning on subsequent memory outcomes proposed in memory allocation and integration models (Schlichting and Frankland, 2017; Schlichting and Preston, 2014), we thus investigated whether and how hippocampal connectivity changes at resting states after initial learning and emotional learning contribute to emotion-charged retroactive memory benefit.”

Page 16 in Discussion: “Neural engagement and circuit connectivity before emotional learning (i.e., hippocampal-LOC connectivity during post-initial-learning rest) serve as a preparatory state that could modulate allocation of new information into overlapping neural populations to support later memory integration (Park and Rugg, 2010; Schlichting and Frankland, 2017; Yoo et al., 2012).”

For the Reviewer’s suggestion on additional new analyses using the average memory performance (or neutral memory), we are afraid that this measure might be contaminated to some extent by emotion-induced retroactive reorganization of previously encoded memories. Given our experimental procedure (i.e., an initial learning phase, followed by an emotional learning phase, and finally a surprise memory testing) and the prominent effects of emotional learning, we thus think subsequent emotional learning and post-emotional-learning rest might overwrite the effect of post-initial-learning hippocampal connectivity changes on final memory performance. Ideally, future studies with memory test before emotional learning are required to directly address how hippocampal connectivity changes link to non-arousal associative memory.

Following the Reviewer’s suggestion, nevertheless, we have also conducted several additional analyses to examine hippocampal connectivity changes from rest-1 to rest-2 that are related to individual differences in non-arousal associative memory. We first conducted a second-level multiple regression analysis for post-encoding hippocampal connectivity changes from rest-1 to rest-2, with the average associative memory (collapsing across aversive and neutral conditions) as a covariate of interest. As expected, this analysis revealed no significant clusters at the same thresholding criteria used for other analyses in our present study (i.e., a height threshold of p < 0.005 and an extent threshold of p < 0.05 corrected for multiple comparisons). Likewise, parallel analysis for memory in the neutral condition also revealed no significant cluster on the whole brain level.

To further explore potential regions related to the average memory at post-initial-learning rest 2 (relative to baseline rest 1), we applied a relatively less stringent height threshold of p < 0.01 and a spatial extent cluster size of more than 30 voxels. We observed clusters in the superior temporal gyrus (STG) and other regions, with greater post-encoding hippocampal connectivity linked to the average associative memory (or memory in the neutral condition) (see Author response image 2 and Author response table 2). We feel that such pattern of weak results might be due to the fact we outlined above – that is, emotional learning and its relevant rests prominently modulated the subsequent memory performance, which covered the effects of initial learning.

Author response table 3
A set of fMRI studies with moderate sample size (N = 16 to 35).
AuthorJournalYearSample Size (effective)Main conclusionCorrelations(including mediating effects)
1Günseli,
Aly
eLife2020N=29Hippocampus and vmPFC support memory-guided attention.Correlation between vmPFC activity and hippocampal activity by skipped_pearson_correlation.m function (Figure 3B).
2Keogh, Bergmann, PearsoneLife2020N=32Cortical excitability is linked to individual differences in the strength of mental imagery.Spearman rank correlations between cortical excitability and imagery strength (Figure 3 and also see Study design in Materials and methods).
3Liu et al.Nature Communication2016N=18Consolidation reconfigures neural pathways underlying the suppression of emotional memories.Correlations between hippocampal functional connectivity/ pattern dissimilarity and behavioral suppression score by prediction analyses (Figure 4 and 6).
4Gruber et al.Neuron2016N=19Post-learning hippocampal dynamics predict reward-related memory advantages.Pearson’s correlations between hippocampal functional connectivity/ reactivation and memory benefits (Figure 2B and 3C).
5Schlichting, PrestonPNAs2014N=35Memory reactivation and hippocampal–neocortical functional connectivity during rest support subsequent learning.Partial correlations between FFA reactivation/FFA-HPC connectivity and memory performance (Figure 2 and 3).
6Wimmer, ShohamyScience2012N=28Hippocampal activation, reactivation, and coupling predict decision bias.Mediating effects of visual reactivation on the relationship between hippocampal activity and striatum activity.
7Tambini,
Ketz, Davachi
Neuron2010N=16Offline hippocampal-cortical interactions relate to subsequent associative memory.Pearson’s correlations between offline hippocampal−LO correlations and associative memory (Figure 3D).
Author response table 4
Statistical power for each significant correlation in main results (two-tailed tests).
CorrelationsSample Size(n)Significance Criterion(α)Effect size(r)Power(1-β)
Pattern similarityCorrelation between pair-specific pattern similarity and associative memory in aversive condition (Figure 3C)280.050.460.72
Task-dependent functional connectivityCorrelation between hippocampal-FFA connectivity and associative memory in aversive condition (Figure 5E)0.570.91
Correlation between hippocampal-LOC connectivity and associative memory in aversive condition (Figure 5F)0.650.98
Author response image 3
Additional condition-level pattern similarity results.

Bar graphs depict the average pattern similarities in aversive and neutral conditions during the presentations of face-voice associations (i.e., Face + Voice) separately for the bilateral hippocampus, bilateral ventral LOC (vLOC) and bilateral FFA. Error bars represent standard error of mean. Notes: *p < 0.05; **p < 0.01; two-tailed t-tests.

Notes: Regions were derived from the multiple regression analyses on post-encoding hippocampal functional connectivity with average/neutral memory as the covariate of interest. Clusters, significant at a height threshold of p < 0.01 and a spatial extent cluster size of more than 30 voxels, are reported with local maximum T statistic in Montreal Neurological Institute (MNI) space. L, left; R, right.

Given the central goal we clarified above and the additional results above which cannot survive at the general thresholding criteria used for other analyses in our study, we therefore decide not to include them into our revised manuscript. Nevertheless, we have added one sentence to incorporate this point into the Discussion section ( Page 17), and it now reads as follows: “However, we did not find any reliable results of post-initial-learning hippocampal connectivity changes in relation to neutral or averaged memory performance, as subsequent emotional learning and post-emotional-learning rest might overwrite the effect of initial learning on final memory performance. Future studies are required to test memory before emotional learning as well to disentangle post-learning signatures linked to neutral and emotion-charged memory performances separately.”

We hope that the Reviewer now agrees with us on the logic and the central goal of our analyses for post-encoding hippocampal connectivity changes in relation to emotion-charged memory benefit. We would like to thank the Reviewer again for these thoughtful comments and suggestion, which also inspire us to think about the goal and logic of our analyses as well as data interpretation via a more in-depth manner.

Reviewer #3:

In this paper, Zhu and colleagues report a series of studies (2 behavioral and 1 fMRI) designed to test the retroactive effects of aversive associations-- which they call "emotional tagging"-- on memory for previously-learned, overlapping neutral associations. Across the 3 studies, they found that neutral face-object associations were remembered better if the face was later paired with an aversive sound. In the fMRI study, the authors additionally showed that the tagging phase was associated with greater pattern reactivation when new associations were aversive, compared to neutral. They also found changes in functional connectivity with the hippocampus that were associated with the retroactive effects of tagging, both during the tagging phase itself and during a post-tagging resting-state scan.

This paper had several strengths. The behavioral findings were compelling, showing the same general pattern across the 3 studies. The topic is timely, as there is accumulating evidence for the retroactive effects of emotion on memory, but little neural data explaining such effects in humans. The analysis approach included multiple sophisticated methods.

We appreciate the Reviewer’s positive assessment of our work by commending that “this paper has several strengths” and “the topic is timely”.

However, there were also significant weaknesses that limit the impact of the paper.

1. The first major weakness is the sample size of the fMRI study (effective N=28), coupled with the fact that many of the conclusions were based on correlations with individual differences in memory-- including the findings that hippocampal reactivation and changes in functional connectivity predicted retroactive memory benefits. This is a concern because correlations across small sample sizes are less likely to replicate in future work. The mediation model is subject to these same concerns.

We thank the Reviewer for this comment on the sample size of our fMRI study. We have now undertaken several strategies to ensure the reproducibility and robustness of our results. First of all, we would like to take this opportunity to clarify that there is several aspects of results supporting our major conclusion on how neural reactivation and connectivity reconfiguration contribute to the emotion-charged retroactive memory benefit. Specifically, our major findings are derived from the prominent retroactive effects of emotional learning on episodic memory across three separate studies (Figure 1B-G), as well as condition-level/trial-specific reactivation/reinstatement (Figure 2B ) and connectivity changes during both learning (Figure 3B-D) and post-encoding states (Figure 4) in the aversive (vs. neutral) condition. Our major conclusions are thus based on the main and/or interaction effects from ANOVAs and stringent whole-brain analyses. The brain-behavior correlations of memory with reactivation (Figure 2C) and functional connectivity (Figure 3H-K) serve as complementary and exploratory results to support our observed specificity of emotion-charged retroactive memory effects. In a word, our main conclusions still hold, even if we toning down our original claims on brain-behavior correlations.

Secondly, we feel that the brain-behavior correlations identified in our study are robust, even with a moderate sample size of 28 participants. The sample sizes (i.e., from 26 to 30 participants) of our three studies were pre-determined by power analysis using G*Power 3.1, which would give us power of around 85-90% for a moderate repeated-measures ANOVA, consistent with many studies with similar analyses on neural reactivation and connectivity (Gruber et al., 2016; Liu et al., 2016; Schlichting and Preston, 2014; Tambini et al., 2010; Wimmer and Shohamy, 2012). These studies with a similar sample size have demonstrated robust correlations of brain activation (or connectivity) patterns with individual differences in memory performance (please see Author response table 3). Besides, we agree that a larger sample size would improve the reproducibility of brain-behavior correlations in general. However, large sample sizes are not a substitute for good hypothesis testing according to the fallacy of classical inference outlined by Karl Friston (Friston, 2012, 2013). It is because large sample sizes increase the probability of rejecting the null hypothesis under trivial treatment effects. A significant result in a proper small sample indicates that the treatment effect is larger than the equivalent result with a large sample.

Thirdly, we have now undertaken three steps to ensure the robustness of our correlation analyses and to improve the integrity of our logic in the revised manuscript. (1) We have implemented a machine learning approach based on linear regression models in combination with balanced four-fold cross-validation algorithms to confirm the robustness of our observed correlations (please see Figure 2C and Figure 3H-J). This approach has been widely used in small samples to confirm the robustness of the predictive relationship between an independent (input) variable and a dependent (outcome) variable. This approach can nicely complement the conventional regression models which are sensitive to outliers and have no predictive value (Cohen, 2010; Geisser, 1993; Qin et al., 2014; Supekar et al., 2013). We have now reported robust r (predicted, observed), estimated based on the average of four repetitions of the four-fold cross-validation procedure, for each correlation and also conducted further analyses (i.e., Steiger’s difference test; please also see our response to points 1a and 2b above) based on it. (2) Referring to some recent studies published on eLife (Günseli and Aly, 2020; Keogh et al., 2020), we have now calculated post-hoc power for each significant correlation in our main results. As shown in Author response table 4, the connectivity correlations with large effect sizes (r > 0.5) give high power more than 85% (Cohen, 1992, 2013). The similarity correlation with a moderate effect size (r > 0.45) also gives an acceptable power of 72%. These results statistically prove the power of our sample to detect individual differences. (3) We have implemented a bootstrapping procedure for our mediation analyses to gain the sensitivity and robustness of statistical estimates in small-to-moderate samples (Preacher and Hayes, 2008; Shrout and Bolger, 2002). Such bootstrap approach has been widely used in the field, because it does not impose the assumption regarding to the normal distribution of the sampling data which is usually required by the parametric approaches. In addition, it is helpful for gaining statistical power with small sample sizes and could yield more accurate estimates of the indirect effect standard errors than other conventional approaches (MacKinnon et al., 2004; Shrout and Bolger, 2002).

In sum, although we agree that a larger sample size would strengthen the reproducibility pertaining to our brain-behavior correlation results, it is very challenging to scan sufficient participants during the COVID-19 pandemic. Therefore, we adopted the above-mentioned steps to address this concern on sample sizes for our brain-behavior correlation analyses. We have clarified our sample size and statistical power in the Methods section of the revised manuscript (Page 17), and it now reads as follows: “The sample sizes across three studies were estimated by a power analysis using G*Power 3.1, which yielded the power of around 85-90% (i.e., from 26 to 30 participants) for a moderate repeated-measures ANOVA, consistent with many memory studies (Gruber et al., 2016; Liu et al., 2016; Schlichting and Preston, 2014; Tambini et al., 2010; Wimmer and Shohamy, 2012). In Study 3, the sample size of 28 valid participants could give us power more than 70% for a moderate correlation (i.e., r > 0.45) (Cohen, 1992, 2013).” We have also reported the effect size (i.e., r (predicted, observed)) for each correlation in the revised manuscript (Figure 2C and Figure 3H-J). We hope that these steps would strengthen the Reviewer’s confidence on the robustness of our observed brain-behavior correlations. Besides, we have also explicitly acknowledged in the Discussion section of our revised manuscript (Page 17), that: “In addition, larger sample size would be helpful to reinforce the reproducibility of our observed brain-behavior association effects in future studies.”

2. The second major weakness is a lack of theoretical clarity regarding the mechanisms supporting the retroactive memory benefit. Two main ideas are introduced: the idea of behavioral tagging (i.e., as related to synaptic tag-and-capture models) and the idea that reactivation plays an important role in memory allocation and integration. I see these as two separate theoretical perspectives that could make different predictions here, which may have been the authors' intention. However, they are not clearly set up as competing hypotheses, nor are they clearly integrated into a unified account. This made it difficult to interpret the results in light of either account.

We appreciate the Reviewer for the opportunity to clarify the theoretical mechanisms supporting our observed retroactive memory benefit. We have now rewritten the Introduction section of our revised manuscript to clarify our theoretical perspectives and hypotheses. As our response to Q1-1 above, we actually intended to combine both behavioral tagging and memory allocation/integration models (including sensory preconditioning suggested by Reviewer 1) into a unified account from multiple neurobiological levels. Considering new results from additional analyses (also see our response to point 1), however, our observed immediate trial-specific retroactive benefit for associations with high-vividness ratings (i.e., presumably strong memories) is more consistent with sensory preconditioning literature and memory integration models. This clearly differs from the delayed retroactive memory benefit for initial weak memories according to the conventional synaptic tag-and-capture (STC) and behavioral tagging models. Our interpretation from a perspective of sensory preconditioning and memory integration is further supported by evidence from our fMRI data with emotion-induced increases in trial-specific pattern reinstatement/reactivation and emotion-charged online/offline hippocampal-neocortical reorganization. Thus, our central goal now is to put forward a new alternative mechanism (i.e., sensory preconditioning as well as memory integration) underlying trial-specific associative learning circumstance in our revised manuscript, which cannot be readily explained by the behavioral tagging models.

To better clarify our theoretical perspectives and hypotheses, we have now rewritten the Introduction section of our revised manuscript (Page 1-3). In brief, we first introduced a set of well-known phenomena of how emotion reshapes episodic memory, especially emotion-charged retroactive memory benefits (Holmes et al., 2018; Shohamy and Daw, 2015; Wong et al., 2019), and raised an open question on its underlying neurocognitive mechanisms in our integrated memory system. We then focused on the emotion-charged event/trial-specific retroactive effect on episodic memory, which always occurs in our daily life but could not readily accounted by conventional STC and behavioral tagging models (Ballarini et al., 2009; Clewett et al., 2022; Dunsmoor et al., 2015; Takeuchi et al., 2016). Thus we introduced sensory preconditioning literatures, which allows us to investigate the trial-specific effect on associated events at behavioral level and provide more compelling evidence with this effect (Brogden, 1939; Kurth-Nelson et al., 2015; Li et al., 2008; Sadacca et al., 2018; Sharpe et al., 2017; Wimmer and Shohamy, 2012; Wong and Pittig, 2021). Finally, we brought up memory allocation and integration models, which have been well proposed to accommodate sensory preconditioning (Holmes et al., 2021; Schlichting and Frankland, 2017; Schlichting and Preston, 2015; Shohamy and Daw, 2015; Shohamy and Wagner, 2008; Wong et al., 2019), to understand the potential mechanisms underlying the emotion-charged trial-specific retroactive memory benefit at neural level.

We have also adapted the Discussion section of our revised manuscript ( Page 14) to interpret our behavioral and fMRI findings mainly through the lens of sensory preconditioning and memory integration models, and discuss the differences between our findings and prior results reported in behavioral tagging studies.

We feel that our revisions have now improved the theoretical clarity regarding the mechanisms supporting the retroactive memory benefit substantially. We would like to thank the Reviewer again for raising this concern which helped us improve our manuscript.

3. The terms "reorganization" and "reconfiguration" are used to describe the functional connectivity results. I have seen these terms used to refer to network-level changes, but they overstate the functional connectivity differences here, which are simply changes in the maps resulting from a seed-based functional connectivity analysis.

We thank the Reviewer for raising this concern. In our current study, we intend to opt for the terms “reorganization” and "reconfiguration" for two reasons. First, we intend to refer it to “memory reorganization” – that is, episodic memories can be reorganized according to their future significance in support of flexibility and adaptivity (Ritchey et al., 2016; Tambini and Davachi, 2019). Specifically, memory reorganization in our study represents an emotion-charged neurocognitive mechanism, by which neutral episodic memories can be strengthened by subsequent emotional learning, and reorganized into an integrated network to protect from forgetting. Second, we feel that “reorganization” can, at least in part, reflect changes in hippocampal-neocortical functional connectivity in our study, as characterized by (a) an increase in online hippocampal-neocortical functional coupling modulated by the amygdala during emotional learning phase, and (b) a shift of hippocampal-neocortical intrinsic functional connectivity from local stimulus-sensitive neocortex to more distributed prefrontal and posterior parietal areas during post-learning rest. A similar idea of brain functional reorganization has been demonstrated by several previous studies on learning, memory, and among other domains (Bassett et al., 2011; Liu et al., 2016; Mutso et al., 2014; Qin et al., 2014; Sevinc et al., 2019; Zhuang et al., 2022). As such, we agree that the term “reorganization” in our current study appears to reflect changes in hippocampal functional connectivity with other brain regions.

We have now adapted the entire manuscript accordingly to better clarify the use of these two terms when appropriate. We have also adjusted our original statements to avoid potential overstatements or claims on brain network reorganization and reconfiguration in our revised manuscript, given the fact that we only assessed changes in hippocampal-seeded connectivity during initial/emotional learning phases and post-learning rests. We are also happy to replace “reorganization” with another more appropriate term in the title if the Reviewer has any better suggestion. We thank the Reviewer again for bringing this point into our attention, as this could help us improve the scrutiny of using “reorganization” and “reconfiguration” in our revised manuscript.

4. It would be more informative to examine pattern reactivation in face-selective and object-selective areas separately (here, they appear to be lumped together), since only reactivation of object-selective areas should be taken as "pure" evidence of learning-phase reactivation, due to the overlap in faces shown during the learning and tagging phases. If there's an increase in pattern similarity in face-selective areas, this could be explained by changes in attention to the visual stimuli associated with the aversive sound rather than reactivation per se.

This is indeed a good point. Following the Reviewer’s suggestion as well as similar comments by two other Reviewers, we have now conducted several trial-specific multivoxel pattern similarity analyses for face-selective FFA and object-selective LOC separately (please also see our responses to point 3 and point 1). As shown in Figure 2 and Figure 2—figure supplement 4, these analyses revealed that emotional learning promoted trial-specific reactivation of initially encoded memories rather than broad category representation in the hippocampus and LOC but not in FFA, contributing to the emotion-charged retroactive memory benefit. We have incorporated these new results into the Results section (Page 7). We have also added several sentences to clarify this point in the Discussion section of our revised manuscript, and it now reads as follows (Page 15): “The object-sensitive LOC showed an increase in trial-specific reactivation of object stimuli, which could reflect reinstatement of corresponding initial face-object associations during emotional learning (Hofstetter et al., 2012; Tambini et al., 2010). But the face-sensitive FFA could not provide pure evidence of reactivation since face information was presented during both phases. These results suggest task-related regional specificity of the observed emotion-charged trial-specific reactivation. In addition, the marginally significant emotional effects for category representation (i.e., across-pair similarity) found in the hippocampus, LOC and FFA might be due to more attention in the aversive (vs. neutral) condition generally attracted by the screaming stimuli, rather than trial-specific reactivation. This explanation is also supported by our condition-level similarity results, showing strengthened reactivation only occurred after the onset of aversive (vs. neutral) voices during emotional learning.”

We feel that our claims on regional specificity of trial-specific reactivation/reinstatement are now more clearly supported. To keep consistency with these reactivation analyses, we have also examined hippocampal connectivity in the FFA and LOC areas separately in the Results section of our revised manuscript (Figure 3). These analyses yielded even more robust and interesting results as compared to our original analyses by lumping these ROIs together. We would like to thank the Reviewer again for this excellent suggestion!

5. The prediction approach was a nice addition here, in that it can help to determine which effects are robust across subjects. However, it appears that they were based on clusters that were already deemed significant through conventional statistical analyses (e.g., see lines 794-795), which suggests that they are biased by the group effects.

We thank the Reviewer for this specific comment. For post-encoding hippocampal connectivity data, significant clusters were indeed pre-selected from whole-brain analyses (i.e., interaction effects in relation to memory in the aversive than neutral condition). We would like to clarify that the predication analyses were implemented only for the confirmatory and visualization purposes. To avoid the potential bias or double dipping, we have now decided to only report the partial correlations with no further statistical inferences, as suggested by the Reviewer #1’s minor comment Q1-7. The correlation plots have now moved into the Supplemental Materials (Figure 4—figure supplement 1).

It is worth noting that we have also conducted the prediction analyses to examine whether neural reactivation and online functional connectivity measures (see Figure 2C and Figure 3H-J) could predict subsequent memory performance. Neuroimaging data in these analyses were derived from significant clusters through the unbiased main effects of encoding (i.e., all encoding trials vs. fixation during each learning phase) and emotional learning (i.e., trials in the aversive vs. neutral condition). This approach is thus independent from the correlation variables of interest. We have now added several sentences to clarify the prediction analyses in the Methods section of our revised manuscript (Page 24). It now reads as follows: “We used a machine learning-based prediction algorithm with balanced fourfold cross-validation (S. Qin, Cho, et al., 2014) to confirm the robustness of relationships of reactivation and online functional connectivity with memory performance. This prediction analysis complements conventional correlation models which are sensitive to outliers and have no predictive value (Cohen, 2010; Geisser, 1993; Supekar et al., 2013).”

We thank the Reviewer again for prompting us to clarify the purposes of our prediction analyses.

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[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #1 (Recommendations for the authors):

The authors did a truly outstanding job responding to the concerns that I raised in the first review. I sincerely believe that the new introduction and discussion provide a much more emperically-motivated depiction of the prior literature, and does a stand-out job delineating these findings from other related concepts such as behavioral tagging.

I was also excited to see such clear results from the additional analyses run. The trial-specific reactivation results give a much more clear picture of the types of reactivation supporting their retroactive memory effects, and the extent of the specificity is quite compelling.

Similarly, the additional controls of the post-encoding analyses were quite strong. One point did emerge that was interesting, which was that there were negative correlations with neutral memory. These findings, albeit with some speculation, suggest that there is a prioritization of related emotional information rather than just an enhancement, meaning there may be a trade-off with neutral information. This would be very aligned with the circuits of interest and the role of noreadrenaline (see Clewett & Murty, 2019). While not completely necessary for this manuscript, additional discussion of this feature of the data that emerged from the new analyses could be included.

We thank the Reviewer for the enthusiasm and positive evaluation of our revised manuscript. We are encouraged by the Reviewer’s commendation that “the authors did a truly outstanding job.” We also appreciate the Reviewer for this thoughtful suggestion on interpreting new results from our post-encoding analyses. We have added several sentences in the Discussion section to acknowledge the possible trade-off between emotional and neutral information during post-encoding periods. The reference suggested by the Reviewer has also been cited appropriately. It now reads as follows:

Page 16: “Interestingly, hippocampal-neocortical connectivity changes involved in this reorganization positively correlated with memory in the aversive condition, but negatively correlated in the neutral condition (i.e., partial correlations controlling for memory in the other condition). It is possible to speculate that the emotion-charged retroactive memory benefit might not only reflect an enhancement of emotion-related information but also a suppression of other neutral information. These findings point toward a potential trade-off between prioritization of emotion-related memory and neglection of mundane neutral memory, aligning with the activity of locus coeruleus-norepinephrine system during post-encoding periods (Clewett and Murty, 2019).”

Reviewer #2 (Recommendations for the authors):

The manuscript by Zhu et al. has improved with revision; I appreciate the addition of the more specific trial-level reactivation analysis which more directly tests the notion that reactivation is a mechanism supporting retroactive emotional memory enhancements. The authors have responded to the prior concerns thoroughly. New details were added regarding ROI definition, in response to prior comments. These new details currently limit my enthusiasm for the manuscript due to the lack of straightforward presentation of some of the analyses/results in the paper: how regions in Figures 4-6 were identified (all other results/analyses are clearly described; see below). Otherwise, the paper makes an important contribution to the field.

We appreciate the Reviewer for the positive evaluation of our additional analyses and revised manuscript. We are pleased that the Reviewer values “this paper making an important contribution to the field”.

A primary concern is the way regions were isolated/identified in the analyses performed after the pattern similarity (those in Figures 4-6). I am not sure whether the FFA/LOC was defined once (i.e. those used in Figures 2-3) and then voxels within those regions were isolated showing connectivity effects for analyses in Figures 4-6, or whether separate regions were isolated across each analysis and are referred to with a common label (implying they are the same). This stems from the second paragraph of the ROI definition section (Methods) which I found to be unclear. As written, it is *implied* that FFA and LOC are re-defined in subsequent analyses "FFA and LOC were derived from a group contrast map of the aversive relative to neutral condition" and "significant clusters (i.e. LOC…) … were derived from the group-level multiple regression analysis on connectivity…". If the ROIs are re-defined in different analysis, it is misleading to use the same label to refer to them across analyses (which implies that they are the same regions or would at least be restricted to a common definition). Please both (1) clarify the approach because it is still not clear to me how the ROIs were defined for connectivity analysis (i.e. did you search for specific contrast within the original FFA/LOC definitions, or were these ROIs re-defined for each analysis within the broader anatomical mask from WFU pickatlas) and (2) the description of the ROIs/results in the main manuscript text should not be misleading as to how the analysis is conducted. If separate regions are isolated for different analyses, then this should be reflected in the labels used for these regions (they should not all have the same label which imply face and object processing regions if they are isolated by other means).

The ROI definition issue raises possible problems of non-independence and multiple comparisons corrections which are currently obfuscated given that the analysis steps are not spelled out. If the 'FFA' and 'LOC' regions shown in Figure 4 were defined from showing greater functional connectivity w/ the hippocampus during aversive vs. neutral trials (not how these regions are typically defined), then the results reported in Figure 4C are non-independent and inferential tests (ANOVAs) should not be performed on this data as it is circular. It is also stated that the gPPI was performed separately for initial encoding and emotional learning phases and clusters were isolated from each – presumably the clusters shown in Figure 4 were isolated from the emotional learning analysis since they show a strong effect in that phase? Please state which analysis they were defined from. The problem of non-independence seems to be remedied in Figure 6 since no statistics are reported and just brain regions are shown. Another possible issue is multiple comparisons corrections, especially *if the FFA/LOC definitions are not carried forward into subsequent analyses*. The original ROI definition does not include an explicit correction but uses a stringent threshold of P<.0001, 30 voxels (fine for ROI definition such that ROIs are interrogated in later analyses). But this thresholding procedure is referenced in subsequent analyses, so presumably all regions isolated in Figures 4-6 are using this criterion. Thus it needs to be specified how this criterion satisfies multiple comparison/family wise error correction / was chosen. Moreover, please clarify if the FFA/LOC regions in Figures 4-6 were also constrained anatomically by the wfu pickatlas regions and neurovault contrasts.

We would like to thank the Reviewer for this opportunity to clarify the definition of FFA/LOC ROIs in our connectivity analyses, as well as possible problems of non-independence and multiple comparisons correction. We have undertaken the following steps to address these concerns.

First of all, we apologize for our unclear descriptions about the functional definition of separate FFA/LOC ROIs in connectivity analyses. Specifically, for task-dependent connectivity analysis in Figures 4-5 (i.e., now Figure 3 in the revised manuscript), we identified ROIs in the left middle portion of FFA (mFFA) (Visconti di Oleggio Castello et al., 2021) and the left superior portion of LOC (sLOC) (Barbieri et al., 2019; Olivo et al., 2019) using a group connectivity contrast during the emotional learning phase, which showed greater connectivity with the hippocampus in the aversive relative to neutral condition, by a height threshold of p < 0.005 and an extent threshold of p < 0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Nichols and Hayasaka, 2003). Likewise, for post-encoding connectivity analysis in Figure 6 (i.e., now Figure 4 in the revised manuscript), we identified the ROI in the left inferior portion of LOC (iLOC) (Barbieri et al., 2019; Olivo et al., 2019) using a group connectivity contrast of Rest 2 relative to Rest 1 in the multiple regression analysis in relation to associative memory performance, by the same threshold criterion mentioned above. Same with pattern similarity analysis, the mFFA, sLOC and iLOC ROIs identified in the task-dependent and post-encoding connectivity analyses in Figures 4-6 (i.e., now Figure 3-4 in the revised manuscript) were further constrained by an anatomically defined mask from the WFU PickAtlas in combination with the mask derived from the Neurosynth platform with “face recognition” or “object recognition” as a searching term (p < 0.01 with FDR correction). We have described the definition of FFA/LOC ROIs for our connectivity analyses with more details in the Methods section, and it now reads as follows:

Page 21: “For task-dependent hippocampal connectivity analysis during initial and emotional learning phases, the right amygdala, left middle portion of FFA (mFFA) (Visconti di Oleggio Castello et al., 2021) and left superior portion of LOC (sLOC) (Barbieri et al., 2019; Olivo et al., 2019) ROIs were defined using a group-level connectivity contrast of the aversive relative to neutral condition during the emotional learning phase, by a height threshold of p < 0.005 and an extent threshold of p < 0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Nichols and Hayasaka, 2003). For post-encoding hippocampal connectivity analysis during three resting phases, significant clusters in the left inferior portion of LOC (iLOC) (Barbieri et al., 2019; Olivo et al., 2019) as well as the bilateral IPL, right PCC, right aPFC and right mPFC were derived from the group-level multiple regression analyses on connectivity contrast maps (i.e., Rest 2 vs. 1, and Rest 3 vs. 2) with interaction effects between aversive and neutral conditions, by the same threshold criterion as task-dependent connectivity analysis above. Same with pattern similarity analysis, the mFFA, sLOC and iLOC ROIs were further constrained separately by the above-mentioned FFA or LOC mask.”

In addition, we have also re-labeled these FFA/LOC ROIs with their specific anatomical portions in the corresponding figures and legends (Figure 2 : ventral LOC and FFA for pattern similarity analysis; Figure 3 : mFFA and sLOC for task-dependent connectivity analysis; Figure 4 : iLOC for post-encoding connectivity analysis), as well as when appropriate throughout the entire revised manuscript.

Second, for the Reviewer’s concern on non-independence problem of task-dependent connectivity analysis in Figure 4 (i.e., now Figure 3 in the revised manuscript). Indeed, our mFFA and sLOC ROIs were derived from the connectivity contrast during emotional learning phase, which identified clusters with greater hippocampal functional coupling in the aversive (vs. neutral) condition. To avoid double dipping and circular analysis, we decided to omit the ANOVAs in original Figure 4E-G and move corresponding bar graphs without any statistics into the Supplemental Materials only for visualization purpose. Our main conclusion of emotion-charged enhancement on hippocampal connectivity with the amygdala and neocortex during emotional learning still holds based on results from the whole-brain contrast without those ANOVAs. Thus, this revision by omitting our original ANOVAs results does not change our main conclusion. In addition, we also conducted a parallel control PPI analysis for the initial learning phase. As expected, this analysis revealed no any emotion-charged effect (i.e., aversive vs. neutral) during initial learning in the mFFA and sLOC.

It is worth to note that our functionally defined mFFA and sLOC ROIs, according to the aversive (vs. neutral) condition during emotional learning, were further used to investigate emotion-induced connectivity changes in relation to associative memory (i.e., later remembered vs. forgotten) in the original Figure 5 (i.e., now Figure 3E-G in the revised manuscript). Since the definition of these ROIs does not bias toward either remembered or forgotten condition, we thus feel that our analyses in Figure 3E-G differ from double dipping according to Kriegeskorte and colleagues’ view on “the use of the same dataset (contrast here) for selection and selective analysis will give distorted descriptive statistics and invalid statistical inference” (Kriegeskorte et al., 2009). Nevertheless, we agree that our functionally defined ROIs in the present study could somehow bring potential selection bias toward the emotion learning phase, and thus are not fully independent compared to the ideal case (i.e., anatomical ROIs). Given that such non-independence with potential selection bias differs from double dipping and could be acceptable under certain circumstances as discussed by previous studies (Kriegeskorte et al., 2010; Kriegeskorte et al., 2009), we feel that our current approach is state-of-the-art and valid due to three considerations: (1) our central goal is to examine emotion-charged connectivity changes in aversive (vs. neutral) condition on our hypothesized ROIs; (2) these ROIs could not be functionally defined during initial learning without any emotional effect; (3) anatomically defined ROIs might include many irrelevant voxels and reduce statistical sensitivity.

In sum, we have now better clarified our ROI definition and omitted ANOVAs results (original Figure 4E-G) in the Results section, as well as moved corresponding plots into the Supplemental Materials (Figure 3—figure supplement 1). We have also incorporated results from the original Figure 5 into updated Figure 3. In addition, we have explicitly acknowledged potential bias of our functionally defined ROIs in the Limitations section of our revised manuscript. The amended texts read as follows:

Page 9-11 in Results: “Emotional learning enhances hippocampal coupling with the amygdala and neocortex which predicts associative memory for initial neutral events.

To investigate how emotional learning modulates functional interactions of the hippocampus and its related neural circuits involved in memory integration, we conducted a task-dependent psychophysiological interaction (PPI) analysis for the emotional learning phase to assess functional connectivity of the hippocampal seed with every other voxel of the brain (Figure 3A). In line with our priori hypothesis, we identified significant clusters in the right amygdala (Figure 3B), left middle portion of FFA (mFFA; Figure 3C) and left superior portion of LOC (sLOC; Figure 3D), which showed greater functional coupling with the hippocampus in the aversive (vs. neutral) condition during emotional learning (Figure 3—figure supplement 1 for visualization). We further conducted a parallel control PPI analysis for the initial learning phase. This analysis revealed no any reliable emotional effect (i.e., aversive vs. neutral) during initial learning in the three ROIs identified above (Figure 3—figure supplement 1 for visualization). Altogether, these results indicate that emotional learning induces functional connectivity changes with prominent increases in hippocampal-amygdala and hippocampal-neocortical coupling.”

Page 17 in Limitations: “And our functionally defined ROIs may be susceptible to potential selection bias. Independent ROI definition (i.e., anatomically defined ROIs) would reinforce the reproducibility of brain-behavior correlations in future studies.”

Figure 3—figure supplement 1 in Supplemental Materials.

Third, regarding the Reviewer’s concern on correction for multiple comparisons, we would like to clarify that only the bilateral ventral portion of LOC (vLOC) and the bilateral FFA ROIs for pattern similarity analysis were defined using this uncorrected threshold (i.e., a threshold of p < 0.0001 with more than 30 voxels) (Miller et al., 2022). All of other ROIs (i.e., mFFA, sLOC and iLOC) for connectivity analyses were defined using a widely-used corrected threshold. Actually, a threshold of p < 0.0001 with more than 30 voxels is satisfied with a height threshold of p < 0.0001 and an extent threshold of p < 0.05 with family-wise error correction for multiple comparisons, by using nonstationary suprathreshold cluster-size approach based on Monte Carlo simulations for the whole brain with an unbiased, anatomically defined, gray matter mask (Nichols and Hayasaka, 2003). We used this extremely stringent threshold criterion to restrict vLOC and FFA ROIs for pattern similarity analysis by the two following reasons: (1) these ROIs were defined by the activation contrasts of all encoding trials relative to fixation, which resulted in a large area of significant clusters; (2) we aim to specify the most engaged regions activated during initial learning and potentially reactivated during emotional learning. Therefore, this stricter correction for multiple comparisons could help us avoid overlaps and identify more specific ROIs. We have now updated the correction criterion in the Methods section. We have also added several sentences to describe this univariate activation analysis and its results in the Supplemental Materials (Figure 2—figure supplement 1). The amended texts read as follows:

Page 20 in Methods: “To investigate the emotion-charged reactivation in both condition- and trial-level pattern similarity analyses, we identified the bilateral hippocampal, bilateral ventral LOC (vLOC) and bilateral FFA ROIs by the overlapping area of two group-level univariate activation contrasts of face-object association encoding (i.e., initial learning) and face-voice association encoding (i.e., emotional learning) separately relative to fixation (i.e., all encoding trials vs. fixation during each learning phase), using a stringent height threshold of p < 0.0001 and an extent threshold of p < 0.05 with family-wise error correction for multiple comparisons based on nonstationary suprathreshold cluster-size distributions computed by Monte Carlo simulations (Miller et al., 2022; Nichols and Hayasaka, 2003) (Figure 2—figure supplement 1).”

Figure 2—figure supplement 1 in Supplemental Materials.

Altogether, we would like to thank the Reviewer again for prompting us to clarify our ROI definition and potential problems of non-independence and multiple comparisons correction. These comments are helpful to improve the integrity of our data analyses and results especially for Figures 4-6 (i.e., now Figure 3-4 in the revised manuscript). We hope that our above responses and revisions have increased your confidence on the clarity of our ROI definition and the presentation of corresponding analyses and results.

Reviewer #3 (Recommendations for the authors):

In this version of the manuscript, Zhu and colleagues have revised their introduction and discussion to situate their experiment within the framework of sensory preconditioning, rather than behavioral tagging. I found the theoretical framework to be much better developed in this revision, and the results link more clearly to the existing conditioning and memory integration literature. Another major revision was the inclusion of a trial-specific reactivation analysis, in which they showed that trial-specific hippocampal patterns were reinstated during the emotional learning phase. This could provide a mechanism by which initially-neutral associations are strengthened through emotional learning. This is an interesting and potentially important result.

In reading the manuscript again, I was impressed with the robust behavioral findings, and I thought the pattern similarity analyses were largely convincing (see one comment below).

We appreciate the Reviewer’s positive assessment of our work by commending that “I was impressed with the robust behavioral findings” and “the pattern similarity analyses were largely convincing”. We also appreciate thoughtful comments that improved our manuscript.

I remain concerned that there is too much emphasis placed on the results based on across-subject correlations, given the relatively small sample size (N=28). This includes the structural equation model as well as the results of the resting-state functional connectivity analyses. I am more convinced by the prediction analyses that follow up on results from the pattern similarity and task-related connectivity analyses. The resting-state analyses are particularly susceptible to the problems of underpowered correlations because they were computed across all voxels in the brain (see Marek et al. 2022 Nature for further discussion). In their response to the previous reviews, the authors described the across-subject correlation analyses as "exploratory" and complementary to the main lines of evidence. Yet this is not how they are presented in the paper itself, where the SEM and resting-state analyses are highlighted as key findings (e.g., see lines 558-563 in the results summary on p. 29). At minimum, more caution should be expressed throughout, with analyses clearly marked as exploratory.

We appreciate the Reviewer for constructive comments and helpful suggestions on across-subject correlations and the sample size. We apologize for our inappropriate highlights of the across-subject correlation results from structural equation modeling and resting-state functional connectivity analyses. Indeed, these results should be described as “exploratory” and “complementary” to our main findings. We have now undertaken four following steps to address this concern. First, considering our hypothesis and conclusion on the mediating effect, we have now decided to focus on results from the mediation analysis rather than “structural equation modeling” (please see our response to point 2 below for details). Second, we conducted additional prediction analyses for resting-state functional connectivity data to confirm the robustness of originally reported across-subject correlation results. We have added several sentences to describe these new prediction analyses and results in the Results section. Given that our manuscript is dense with results and to avoid redundancy, we decided not to include the prediction plots in the revised manuscript, which show very similar patterns as correlation plots (Figure 4—figure supplement 1). It now reads as follows:

Page 13: “As a complement, the machine learning-based prediction analyses for post-learning hippocampal connectivity changes revealed very similar patterns of positive predictions for associative memory in the aversive condition (iLOC: r(predicted, observed) = 0.47; IPL: r(predicted, observed) = 0.54; PCC: r(predicted, observed) = 0.51; aPFC: r(predicted, observed) = 0.52; mPFC: r(predicted, observed) = 0.48; all p < 0.005 while controlling for memory in the neutral condition), but negative predictions in the neutral condition (iLOC: r(predicted, observed) = -0.45; IPL: r(predicted, observed) = -0.55; PCC: r(predicted, observed) = -0.50; aPFC: r(predicted, observed) = -0.53; mPFC: r(predicted, observed) = -0.51; all p < 0.006 while controlling for memory in the aversive condition).”

Third, we fully agree with the Reviewer that reproducible brain-wide association studies (BWAS) require a relatively large sample size, especially for detecting associations between inter-individual differences in brain structure or function and complex cognitive/mental health phenotypes (Marek et al. 2022). In our current study, however, we actually conducted hypothesis-driven brain-behavior association analyses with a particular focus on functional brain metrics (i.e., RSA, connectivity) and emotion-charged memory performance – those variables were directly derived from our dedicated fMRI experimental manipulation, rather than from general cognitive/mental health phenotypes that are unrelated to the fMRI design. Nevertheless, we acknowledged that our limited sample size for the mediation analysis and the resting-state connectivity analysis in relation to memory performance is susceptible to the problem of underpowered correlations. As suggested, we have now explicitly acknowledged our mediation and resting-state analyses as “exploratory” and “complementary” throughout the entire revised manuscript. The amended texts read as follows:

Page 3 in Introduction: “A set of multi-voxel pattern similarity, task-dependent functional connectivity and machine learning-based prediction analyses, in conjunction with exploratory task-free functional connectivity and mediation analyses, …”

Page 11-12 in Results: “Therefore, we assumed a potential mediatory pathway among these variables, … Based on above theoretical motivation as well as empirical observations, we further implemented an exploratory mediation analysis ….

… Altogether, these exploratory results indicate that increased hippocampal-amygdala coupling might indirectly account for emotion-charged associative memory, likely mediating by hippocampal-neocortical couplings during emotional learning.”

Page 12-13 in Results: “Furthermore, to explore how offline hippocampal connectivity changes at resting states prior to and after emotional learning contribute to emotion-charged retroactive memory benefit, … Specifically, the exploratory whole-brain analyses revealed that… These exploratory results indicate a potential shift of post-learning hippocampal connectivity from the object-sensitive lateral occipital cortex to more distributed transmodal prefrontal and posterior parietal areas, which predicts emotion-charged retroactive memory benefit.”

Page 13 in Discussion: “Complementally, hippocampal-amygdala coupling positively predicted the emotion-charged retroactive memory benefit, mediating by increased hippocampal-neocortical interactions. Moreover, we explored a potential shift of hippocampal-neocortical connectivity contributing to the emotion-charged retroactive memory enhancement during post-learning rests, …”

Page 15-16 in Discussion: “Third, we explored that increased hippocampal-neocortical coupling could mediate the positive relationship between hippocampal-amygdala coupling and emotion-charged retroactive memory enhancement. This exploratory mediation effect suggests …”

Last but not least, we explored a potential hippocampal-neocortical functional reorganization during post-learning rests predictive of emotion-charged retroactive memory benefit, …”

Fourth, we have also noted the relatively small sample in the Limitations section of our revised manuscript. It now reads as follows:

Page 17: “Second, our relatively small sample size in fMRI Study 3 would be underpowered to detect individual differences in across-subject correlations and mediation analyses. A larger sample size would reinforce the reproducibility of brain-behavior correlations in future studies.”

For the structural equation model, the authors used a bootstrapping technique that may improve the ability to estimate direct and indirect effects even in small sample sizes. The model fit indices, however, seem to have been computed in the standard way. Given that the analysis may be underpowered to detect model misspecification, the authors should tone down their description of the model fit (both for the primary model as well as the alternative tested models).

We agree with the Reviewer that the model fit indices computed in a standard way may be underpowered to detect model misspecification. We have now undertaken three steps to mitigate this concern. First, we decided to focus on the mediation effect rather than “structural equation modeling”. In other words, our main goal is to investigate whether hippocampal-mFFA/sLOC connectivity could account for the indirect relationship between hippocampal-amygdala connectivity and emotion-charged memory performance. Second, as the Reviewer suggested, we have now toned down our descriptions of the model fit, by removing the model fit indices for both primary model and alternative tested models in the Results, Methods sections and the Supplementary Materials (Figure 3—figure supplement 3). Third, although previous fMRI studies also conducted mediation analyses on similar sample sizes (Hare et al., 2010; Jimura et al., 2010) as our current study, we agree that small sample size limits the power to detect individual differences in across-subject correlations. Therefore, we have now clearly marked the mediation analysis as an “exploratory analysis” throughout the revised manuscript (please also see our response to point 1 above). The amended texts read as follows:

Page 11 in Results: “This exploratory mediation analysis revealed two significant mediating effects on the positive relationship between hippocampal-amygdala connectivity and memory outcome …”

Page 24 in Methods: “We conducted a mediation analysis to further explore how emotional learning affects initial associative memory through functional amygdala-hippocampal-neocortical pathways during emotional learning. … The mediating effect of hippocampal-neocortical connectivity was tested by a bias-corrected bootstrap with 1,000 samples, which could improve the sensitivity and robustness of statistical estimates in small-to-moderate samples (Preacher and Hayes, 2008; Shrout and Bolger, 2002).”

In the pattern similarity analyses, reinstatement was observed at the onset of the voice but not in response to the face cue alone. Yet it appears that the voice onset occurred only 2s after the onset of the face cue, which is the equivalent of one TR. With that temporal resolution, it shouldn't be possible to reliably separate these two signals in time, and including them in the same model may lead to unstable parameter estimates. How have the authors addressed this?

Indeed, it is challenging to reliably separate the two BOLD-fMRI signals associated with face cues and face-voice pairs with only 2s intervals. First of all, we would like to clarify that we did not actually intend to separate these two signals in the same model. Instead, we included the presentations of face cues in aversive and neutral conditions into the model as covariates of no interest, in order to control for brain activity associated with the process of face cues. This approach has been used in previous studies to account for brain activity patterns associated with tasks or events of no interest (Ballard et al., 2011; Costumero et al., 2015; Crockett et al., 2017). Our main goal of this analysis is to investigate the emotional effect induced by aversive voices during the presentation of face-voice associations, while controlling for brain activity linked to the presentation of face cues. Second, as suggested by the Reviewer #2’s minor comment Q2-13: “this is essentially captured by showing a main effect of emotion in the Figure 3 results” (i.e., now Figure 2 with trial-level reactivation results in the revised manuscript), we have now moved these condition-level reactivation results into the Supplementary materials (Figure 2—figure supplement 2 ). Thus, this potentially unreliable signal separation would not affect our main conclusions derived from trial-level pattern similarity analysis. Third, we have also conducted an additional GLM analysis of emotional learning phase which only included the presentations of face-voice associations in aversive and neutral conditions as two regressors of interest, without considering the presentation of face cues. This additional analysis revealed almost identical pattern of emotional effect for each ROI as our current analysis (hippocampus: t(27) = 2.53, p = 0.017, dav = 0.24; vLOC: t(27) = 2.14, p = 0.042, dav = 0.14; FFA: t(27) = 2.98, p = 0.006, dav = 0.19; see Author response image 3). To avoid redundancy, we decided not to include these results in the revised manuscript.

Fourth, in response to the Reviewer’s comment, nevertheless, we have now noted this issue in the Limitations section of our revised manuscript. It now reads as follows:

Page 17: “Third, it is challenging to reliably separate neural signals associated with "face cues" and "face-voice associations" due to only an interval of 2 seconds. Future design with longer and jittered intervals may resolve this issue.”

There are a couple of places in the Results where non-significant results are described as significant, in cases when one ROI shows a significant effect but the other doesn't: the LOC interaction in line 295, the amygdala correlation in line 433. Conclusions should also be updated to refer only to those findings that are significant.

We thank the Reviewer for pointing this out. We have gone through the entire manuscript to carefully check and change the descriptions of marginally but not significant results (i.e., 0.05 < p < 0.08). And we have also updated the corresponding conclusions accordingly. The amended texts read as follows:

Page 17 in Results: “These analyses revealed significant main effects of Emotion in the hippocampus (F(1, 25) = 9.48, p = 0.005, partial η2 = 0.28) and vLOC (F(1, 25) = 9.34, p = 0.005, partial η2 = 0.27), as well as a significant Emotion-by-Measure interaction effect in the hippocampus (F(1, 25) = 4.89, p = 0.036, partial η2 = 0.16) and a similar trend but non-significant interaction in the vLOC (F(1, 25) = 3.54, p = 0.072, partial η2 = 0.12), … Post-hoc comparisons (with covariates controlled) revealed significantly higher pair-specific similarity (hippocampus: F(1, 25) = 7.24, p = 0.013, partial η2 = 0.22; vLOC: F(1, 25) = 8.28, p = 0.008, partial η2 = 0.25), but only a non-significant trend of higher across-pair similarity (hippocampus: F(1, 25) = 4.16, p = 0.052, partial η2 = 0.14; vLOC: F(1, 25) = 3.37, p = 0.078, partial η2 = 0.12) in the aversive than neutral condition. We also observed significantly higher pair-specific than across-pair similarity in the hippocampus (F(1, 25) = 4.42, p = 0.046, partial η2 = 0.15) and a similar but non-significant trend in the vLOC (F(1, 25) = 2.96, p = 0.097, partial η2 = 0.11) in the aversive condition, but not in the neutral condition (F(1, 25) < 2.20, p > 0.150, partial η2 < 0.08 in both ROIs). … These results indicate that emotional learning prompts greater trial-specific reinstatement relative to category-level representation in the hippocampus, and it also leads to a similar but non-significant trend in the vLOC.”

Page 9-11 in Results: “A similar trend (though not significant) was also shown in hippocampal-amygdala connectivity (i.e., remembered with high confidence relative to forgotten; aversive: r(predicted, observed) = 0.24, p = 0.076; neutral: r(predicted, observed) = -0.25, p = 0.839). Further Steiger’s tests revealed significant differences in correlation coefficients between two conditions for hippocampal coupling with mFFA (z = 2.13, p = 0.033) and sLOC (z = 2.15, p = 0.032), and a non-significant trend of difference for hippocampal-amygdala coupling (z = 1.79, p = 0.073). These results indicate that emotion-charged hippocampal connectivity with stimulus-sensitive neocortical regions positively predicts associative memory in the aversive but not neutral condition, implying an emotional specificity effect, though hippocampal-amygdala connectivity only shows a similar trend but non-significant effect.”

Page 11 in Results: “According to our empirical observations, memory performance in the aversive condition showed only a trending positive correlation with hippocampal-amygdala connectivity, but highly positive correlations with hippocampal-mFFA/sLOC connectivity.”

Page 15 in Discussion: “Critically, we found the prominent emotional enhancement for trial-specific reactivation (i.e., pair-specific similarity) in the hippocampus and vLOC, but not in the FFA. … In addition, the trending but not significant emotional effects for category representation (i.e., across-pair similarity) found in the hippocampus, vLOC and FFA might be due to more attention in the aversive (vs. neutral) condition generally attracted by the screaming stimuli, rather than trial-specific reactivation.”

Page 15 in Discussion: “Second, although such hippocampal connectivity patterns did not show reliable Emotion-by-Memory interaction effect, results from our prediction analyses revealed that hippocampal connectivity with the (delete: the amygdala and) neocortical regions during emotional learning positively predicted memory for face-object associations in the aversive rather than neutral condition.”

https://doi.org/10.7554/eLife.60190.sa2

Article and author information

Author details

  1. Yannan Zhu

    1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
    2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
    3. Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5935-4282
  2. Yimeng Zeng

    1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
    2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
    Contribution
    Formal analysis, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Jingyuan Ren

    Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7089-6397
  4. Lingke Zhang

    1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
    2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Changming Chen

    School of Education, Chongqing Normal University, Chongqing, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3501-4643
  6. Guillen Fernandez

    Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Shaozheng Qin

    1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
    2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
    3. Chinese Institute for Brain Research, Beijing, China
    Contribution
    Conceptualization, Data curation, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    szqin@bnu.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1859-2150

Funding

National Natural Science Foundation of China (32130045)

  • Shaozheng Qin

National Natural Science Foundation of China (82021004)

  • Shaozheng Qin

National Natural Science Foundation of China (31522028)

  • Shaozheng Qin

Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD1503)

  • Shaozheng Qin

Chinese Scholarship Council (201806040186)

  • Yannan Zhu

National Natural Science Foundation of China (31871110)

  • Changming Chen

National Natural Science Foundation of China (81571056)

  • Shaozheng Qin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (32130045, 82021004, 31871110, 31522028, 81571056), the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD1503), and the PhD scholarship (201806040186) of the Chinese Scholarship Council. We thank Liping Zhuang and Siya Peng for their assistance in data analysis. We also thank Nils Kohn and three reviewers for their valuable suggestions and comments for the manuscript.

Ethics

Informed written consent was obtained from each participant before the experiment.Consent authorisation for publication was also obtained by a written consent form from each individualwho provided his/her identifying image for illustration purpose in the article. The Institutional ReviewBoard for Human Subjects at Beijing Normal University (ICBIR_A_0098_002), Xinyang Normal University(same as above) and Peking University (IRB#2015-09-04) approved the procedures for Study 1, 2 and 3respectively.

Senior Editor

  1. Laura L Colgin, University of Texas at Austin, United States

Reviewing Editor

  1. Thorsten Kahnt, National Institute on Drug Abuse Intramural Research Program, United States

Reviewers

  1. Vishnu Murty, Temple University, United States
  2. Arielle Tambini, Nathan Kline Institute for Psychiatric Research, United States

Publication history

  1. Received: June 18, 2020
  2. Preprint posted: September 9, 2020 (view preprint)
  3. Accepted: December 6, 2022
  4. Accepted Manuscript published: December 8, 2022 (version 1)
  5. Accepted Manuscript updated: December 9, 2022 (version 2)
  6. Version of Record published: January 5, 2023 (version 3)

Copyright

© 2022, Zhu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yannan Zhu
  2. Yimeng Zeng
  3. Jingyuan Ren
  4. Lingke Zhang
  5. Changming Chen
  6. Guillen Fernandez
  7. Shaozheng Qin
(2022)
Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization
eLife 11:e60190.
https://doi.org/10.7554/eLife.60190

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