Working memory (WM) or short-term memory actively retains a small amount of information to support ongoing mental processes (Baddeley, 2012). This core mental faculty relies upon distributed brain regions (Christophel et al., 2017; Eriksson et al., 2015), ranging from lower-level sensory areas (Harrison and Tong, 2009; but see Bettencourt and Xu, 2016) to higher-level frontoparietal networks (Bettencourt and Xu, 2016; Ester et al., 2015; Todd and Marois, 2004; Xu and Chun, 2006). This distributed neocortical network, however, often does not involve the medial temporal lobe (MTL), which is traditionally attributed to long-term episodic memory (Eichenbaum et al., 2007; Squire and Zola-Morgan, 1991). This distinction is grounded in the separation between WM and long-term memory in classic models (Atkinson and Shiffrin, 1968; Norris, 2017) and in early MTL lesion case studies (Milner et al., 1968; Scoville and Milner, 1957). Yet, this classic view is not free of controversy. A growing body of research has suggested that the MTL is involved in tasks that rely on information maintained in WM (Boran et al., 2022; Boran et al., 2019; Hannula and Ranganath, 2008; Johnson et al., 2018; Kamiński et al., 2017; Kornblith et al., 2017; Libby et al., 2014; Liu et al., 2020; Rissman et al., 2008; Xie et al., 2023a; Xie and Zaghloul, 2021). Furthermore, MTL lesions can disrupt WM task performance (Goodrich et al., 2019; Koen et al., 2017; Olson et al., 2006; Warren et al., 2014; Xie et al., 2023a). Despite these recent findings, however, major theories have not considered the MTL as a mechanism underlying WM (Jeneson and Squire, 2012; Sreenivasan and D’Esposito, 2019). First, it is unclear what computational process of the MTL is involved in WM (Sreenivasan and D’Esposito, 2019). Furthermore, the MTL tends to engage more in a WM task when long-term memory becomes relevant, for example when task loads are higher (Boran et al., 2022; Boran et al., 2019; Rissman et al., 2008) or when task stimuli are complex (Barense et al., 2007; Borders et al., 2022; Kamiński et al., 2017; Kornblith et al., 2017; Libby et al., 2014; Liu et al., 2020). As a result, contributions of the MTL to WM are often deemed secondary (Jeneson and Squire, 2012; Sreenivasan and D’Esposito, 2019).

Clarifying this issue requires specifying how the MTL contributes to WM representation and the extent to which this contribution holds even when WM task demand is minimized. Although WM and long-term memory are traditionally considered separate mental faculties, the functional parallels in both types of memory suggest potential shared neural mechanisms (Beukers et al., 2021; Cowan, 2001; Nee and Jonides, 2008; Ruchkin et al., 2003). For example, the ability to retain precise item-specific memory would require the computation to distinguish neural representations of similar information – a process known as pattern separation (Marr, 1971). This aspect of long-term memory is widely thought to emerge from various properties of the MTL’s entorhinal-DG/CA3 pathway (Aimone et al., 2011; Bakker et al., 2008; Cappiello et al., 2016; Ekstrom and Yonelinas, 2020; Korkki et al., 2021; Leal and Yassa, 2018; Marr, 1971; Reagh and Yassa, 2014; Yassa and Stark, 2011), such as abundant granule cells and strong inhibitory interneurons in the hippocampal DG, as well as powerful mossy fiber synapses between the DG and CA3 subfields (Aimone et al., 2011; Sahay et al., 2011). These properties make it possible to enable sparse coding to ensure a sufficient representational distance among similar information (Rolls, 2016; Rolls, 2013). As these hippocampal substructures communicate with other neocortical areas via the entorhinal cortex (Aimone et al., 2011; Leal and Yassa, 2018), there is a proposed gradian of pattern separation along the entorhinal-DG/CA3 pathway to support item-specific long-term episodic memory (Reagh and Yassa, 2014). These ideas are supported by evidence based on animal and human behaviors (Burke et al., 2011; Hunsaker et al., 2008; Ryan et al., 2012), electrophysiological recordings (Leutgeb et al., 2007; Lohnas et al., 2018; Sakon and Suzuki, 2019), and human fMRI (Bakker et al., 2008; Leal and Yassa, 2018; Montchal et al., 2019; Reagh and Yassa, 2014). However, the extent to which the entorhinal-DG/CA3 pathway is involved in WM, especially in humans other than animal models (Gilbert and Kesner, 2006), has remained unknown.

Several challenges faced in past research may add to this uncertainty. For example, it is difficult to infer signals from MTL substructures, especially those within the hippocampus, based on human fMRI using a standard spatial resolution (Bettencourt and Xu, 2016; Ester et al., 2015) or intracranial direct recording with limited electrode coverage (Boran et al., 2019; Johnson et al., 2018; Kamiński et al., 2017; Kornblith et al., 2017). Furthermore, the use of complex task designs with multiple memory items (Borders et al., 2022) might also be suboptimal to reveal item-specific WM information in MTL subregions without being too taxing on the WM storage limit. To investigate these issues, here, we leverage an established retro-cue orientation WM task (Bettencourt and Xu, 2016; Ester et al., 2015; Harrison and Tong, 2009) and a high-resolution fMRI protocol to test the key prediction that the MTL’s entorhinal-DG/CA3 pathway retains item-specific WM information of a simple surface feature. In this task, participants are directed to retain the orientation information of a cued stimulus from two sequentially presented orientation gratings (separated by >20°; Figure 1A). After a short delay (5 TRs; 1TR = 1.75 s), they try to reproduce the cued orientation grating as precisely as possible using the method of adjustment. As participants are retrospectively cued to retain only one item during the delay, they are expected to encode both items but then only keep one in mind during the delay period. This design imposes a task demand on the observer to correctly remember the cued orientation while resisting the interference from the internal representations of other similar orientation gratings. The retention of information selected after encoding over a short delay has been considered a hallmark of WM (Lorenc et al., 2021; Panichello and Buschman, 2021), regardless of the presence or absence of sustained neural activation (Lundqvist et al., 2018; Rose et al., 2016). If the MTL’s entorhinal-DG/CA3 pathway indeed supports this function, it is expected that the recorded delay-period activity should contain more information about the cued item, as compared with the uncued item, even though both items are initially remembered with an equal likelihood (Bettencourt and Xu, 2016; Ester et al., 2015; Harrison and Tong, 2009). If, however, information about the cued and uncued items is equally present during the delay period, the MTL may play a limited role in the representation of task-relevant information in WM but more during the initial encoding.

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Participants’ memory performance is quantified as recall error – the angular difference between the reported and the actual orientations of the cued item (Zhang and Luck, 2008). As the effective memory set size is low at one memory item, participants’ performance is high with an average absolute recall error of 12.01°±0.61° (mean ± s.e.m.). Furthermore, the recall error distribution is centered around 0° with most absolute recall errors smaller than 45° (~97% trials; Figure 1B). These behavioral data suggest that participants in general have remembered high-fidelity orientation information of the cued item during the delay period.

Fine discrimination of remembered WM content in the MTL

Of primary interest, we examined whether precise orientation information of the cued item is retained during WM retention in anatomically defined MTL regions of interest (ROIs; Figure 2A), including the entorhinal cortex (anterior-lateral, aLEC and posterior-medial, pMEC), the perirhinal cortex, para-hippocampus, and hippocampal DG/CA3, CA1, subiculum, as defined in the previous studies (Montchal et al., 2019; Reagh et al., 2017). Additionally, we chose the amygdala as a theoretically irrelevant but adjacent control region, because the involvement of the amygdala for emotionally neutral orientation information is expected to be minimal (Iwai et al., 1990). This allows us to gauge the observations in MTL ROIs while controlling for the signal-to-noise ratio in fMRI blood-oxygenation-level-dependent (BOLD) signals in deep brain structures.

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As recent neural theories of WM have proposed that information retained in WM may not rely on sustained neural activation (Ester et al., 2015; Kamiński and Rutishauser, 2020; Rose et al., 2016), we inspected how the multivoxel activity pattern in each subject-specific ROI is correlated with the retained WM content predicted by the cued orientation gating (Figure 2B). We found that certain voxels in an ROI could respond more strongly to a particular cued orientation, even when the average BOLD activity across voxels does not show preferred coding for a certain orientation (see an example in Figure 2—figure supplement 1). We then assessed the consistency of these stimulus-related multivoxel activity patterns in the MTL and the amygdala control region based on stimulus-based representational similarity analysis. In this analysis, we correlated the angular similarity of every pair of cued orientation gratings with the similarity of the evoked BOLD patterns in these trials. The rationale is that if orientation information is retained within an ROI, the recorded neural data should track the relative angular distance between any two cued orientation gratings (hence fine discrimination Kriegeskorte and Wei, 2021). Informed by the previous research (Ester et al., 2015; Harrison and Tong, 2009), we performed this analysis using the raw fMRI BOLD signals from the middle 3TRs out of the 5-TR retention interval to minimize the contribution of sensory process or anticipated retrieval, hence maximizing the inclusion of neural correlates of WM retention (Postle et al., 2000).

In line with our prediction, we found that stimulus similarity for the cued item was significantly correlated with neural similarity across trials as compared with the null in both the aLEC (t(15) = 4.29, p=6.48e-04, p_Bonferroni = 0.0052, Cohen’s d=1.11, p_boostrap <0.001) and the hippocampal DG/CA3 (t(15) = 3.64, p=0.0024, p_Bonferroni = 0.019, Cohen’s d=0.94, p_boostrap <0.001; Figure 2C). In contrast, stimulus similarity for the uncued item across trials could not predict these neural similarity patterns in these regions as compared with the null (aLEC: t(15) = –0.20, p=0.85, Cohen’s d=–0.05; DG/CA3: t(15) = 0.06, p=0.95, Cohen’s d=0.02; p_boostrap’s>0.50). Furthermore, the evoked neural similarity patterns in these regions were significantly more correlated with the cued item as compared with the uncued item (aLEC: t(15) = 2.66, p=0.018, Cohen’s d=0.69, p_boostrap = 0.015; DG/CA3: t(15) = 3.64, p=0.0024, Cohen’s d=0.94, p_boostrap = 0.0016). While the rest of the MTL showed similar patterns, we did not obtain significant evidence in other MTL ROIs following the correction of multiple comparisons (see Supplementary file 1a for full statistics). Furthermore, neural evidence related to the cued item in the aLEC and DG/CA3 was significantly stronger than that in the amygdala control ROI. This was supported by a significant cue (cued vs. uncued) by region (combined aLEC-DG/CA3 vs. amygdala) interaction effect on the correlation between stimulus and neural similarity patterns (F(1, 15)=4.97, p=0.042, p_boostrap = 0.036). Together, these results suggest that delay-period activity patterns in the entorhinal-DG/CA3 pathway are associated with retrospectively selected task-relevant information, implying the presence of item-specific WM representation in these subregions.

Reconstruction of item-specific WM information based on inverted encoding modeling

To directly reveal the item-specific WM content, we next modeled the multivoxel patterns in subject-specific ROIs using an established inverted encoding modeling (IEM) method (Ester et al., 2015). This method assumes that the multivoxel pattern in each ROI can be considered as a weighted summation of a set of orientation information channels (Figure 3A). By using partial data to train the weights of the orientation information channels and applying these weights to an independent hold-out test set, one can reconstruct the assumed orientation information channels to infer item-specific information for the remembered item – operationalized as the resultant vector length of the reconstructed orientation information channel normalized at 0° reconstruction error (Figure 3—figure supplement 1). As this approach verifies the assumed information content based on observed neural data, its results can be efficiently computed and interpreted within the assumed model even when the underlying neuronal tuning properties are unknown (Ester et al., 2015; Sprague et al., 2018). This approach, therefore, complements the model-free similarity analysis by linking representational geometry embedded in the neural data with item-specific information under a model-based framework (Kriegeskorte and Wei, 2021; Xie et al., 2023b). On the basis of this method, previous research has revealed item-specific WM information in distributed neocortical areas, including the parietal, frontal, and occipital-temporal areas (Bettencourt and Xu, 2016; Ester et al., 2015; Rademaker et al., 2019; Sprague et al., 2016), which are similar to those revealed by other multivariate classification methods (e.g. support vector machine, SVM, Ester et al., 2015). We have also replicated these IEM effects in the current dataset (Figure 3—figure supplement 2).

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Moving beyond these well-established observations in distributed neocortical structures, we found that the amount of reconstructed item-specific information for the cued item during WM retention was also significantly greater than chance level in two anatomically defined MTL subregions, aLEC (t(15) = 4.41, p=5.07e-04, p_bonferroni = 0.0041, Cohen’s d=1.14, p_boostrap <0.001) and the hippocampal DG/CA3 (t(15) = 4.73, p=2.68e-04, p_bonferroni = 0.0021, Cohen’s d=1.22, p_boostrap <0.001; Figure 3B). These effects were specific to the maintenance of the cued item, as information related to the uncued item was not statistically different from chance (aLEC: t(15) = –0.35, p=0.74, Cohen’s d=–0.09; DG/CA3: t(15) = 0.66, p=0.52, Cohen’s d=0.17; p_boostrap’s>0.50) and was significantly less than that for the cued item (aLEC: t(15) = 2.75, p=0.015, Cohen’s d=0.71, p_boostrap = 0.018; DG/CA3: t(15) = 3.83, p=0.0016, Cohen’s d=0.99, p_boostrap = 0.0023). Critically, the amount of information specific to the cued item in the aLEC and DG/CA3 was significantly greater than that in the amygdala control ROI, which is supported by a significant cue (cued vs. uncued) by region (combined aLEC-DG/CA3 vs. amygdala) interaction effect on IEM reconstruction outcomes (F(1, 15)=7.16, p=0.016, p_boostrap = 0.010).

Collectively, results from complementary analytical procedures suggest that the MTL’s entorhinal-DG/CA3 pathway retains precise item-specific WM content for a simple surface feature (e.g. orientation) to allow fine discrimination of different items in the feature space. As such, the stimulus-based prediction of neural similarity is highly correlated with the amount of reconstructed information based on IEM, even though these two analyses are based on different analytical assumptions (e.g. correlation between IEM and representational similarity analysis for the cued item, aLEC: r=0.87, p=0.000012, p_boostrap <0.001; DG/CA3: r=0.78, p=0.00037, p_boostrap <0.001; Figure 3—figure supplement 3).

Reconstruction of WM Item Information in the MTL is associated with recall fidelity

Next, we examined the extent to which WM information retained in the MTL’s aLEC-DG/CA3 circuitry is related to an observer’s subsequent recall behavior. As the angular resolution of the reconstructed orientation information is 20° in the current study, our data therefore suggest that the MTL can distinguish similar orientation information in WM that is at least 20° apart. This neural separation should be consequential for later recall performance, in that trials with greater item-specific information reconstructed from the MTL should be associated with higher WM recall fidelity. To test this prediction, we grouped the trials from each participant into two categories. The first category contained small recall error trials, where participants made an effective recall response within one similar item away from the cued item (absolute recall error <20°; 149±3 trials [mean ± s.e.m.]). Another category contained larger recall error trials (27±3 trials) with absolute recall errors that were greater than 20° but smaller than the 3 standard deviations (SD) of the aggregated recall error distribution (Figure 4A). These trials would capture participants’ imprecise recall responses for the cued item, instead of those with an extra-large recall error that could be attributed to other factors such as attentional lapses (deBettencourt et al., 2019). The two identified categories of trials together account for about 98% of the total trials (i.e. 176 out of 180 trials).

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We then performed the leave-one-block-out analysis to obtain trial-by-trial IEM reconstructions based on delay-period BOLD signals aggregated from the aLEC and DG/CA3. We averaged the IEM reconstructions from the small- and larger-error trials separately. Because trial counts between categories were not balanced, we resampled the data from the small-error trials based on the number of larger-error trials for 5000 times. We took the average of IEM reconstruction across iterations to obtain robust subject-level trial-average estimates with a balanced trial count across different behavioral trial types (Xie et al., 2020a; Yaffe et al., 2014). By contrasting these estimates at the subject level, we found that the small-error trials yielded significant IEM reconstructions for the cued item (t(15) = 4.50, p=4.21e-04, Cohen’s d=1.16, p_boostrap <0.001), whereas the larger-error trials did not (t(15) = 0.03, p=0.98, Cohen’s d=0.007, p_boostrap = 0.90; Figure 4B). Furthermore, the reconstructed WM information in the combined aLEC-DG/CA3 showed better quality in the small-error trials, as compared with that in the larger-error trials (t(15) = 2.45, p=0.027, Cohen’s d=0.61, p_boostrap = 0.032).

In addition to using an empirical criterion to separate in-memory trials from those extra-large error trials susceptible to occasional attentional lapses (deBettencourt et al., 2019), we have also tried another thresholding heuristic. As shown in Figure 1A, most trials from each participant fall within this 45° of absolute recall error (i.e. half of the 90° range), and the trials larger than this number are rare (~5 out of 180 trials). We, therefore, used 45° of absolute recall error as a cut-off to identify the imprecise recall trials that were greater than 20° but smaller than 45° of absolute recall error. We performed the same analysis to obtain trial-by-trial IEM reconstructions based on delay-period BOLD signals aggregated from the aLEC and DG/CA3 as outlined above, and then resampled the same number of trials to estimate the IEM reconstructions for the small-error and larger-error trials (<20° vs. 20° - 45° of absolute recall error). Consistent with the 3-SD heuristic, we found that the small-error trials identified by the 45° cut-off heuristic also yielded significant IEM reconstructions for the cued item (t(15) = 4.34, p=5.74e-04, Cohen’s d=1.12, p_boostrap <0.001), whereas the larger-error trials did not (t(15) = –0.69, p=0.50, Cohen’s d=–0.18, p_boostrap = 0.67). We then contrasted the difference in IEM reconstructions between these small- and large-error trials across participants. We found that IEM reconstruction for the cued item from the combined aLEC-DG/CA3 showed better quality in the small-error trials, as compared with that in the larger-error trials (t(15) = 3.41, p=0.004, Cohen’s d=0.88, p_boostrap = 0.008). Collectively, these results suggest that higher-quality WM representation in the entorhinal-DG/CA3 pathway during the delay period is associated with better subsequent recall fidelity and that this association is robust to the selection of cut-off scores for extra-large recall errors.

Based on high-resolution fMRI, this current study uncovers an often-neglected role of the MTL’s the entorhinal-DG/CA3 pathway in item-specific WM representation at a minimal task load. Our data suggest that the entorhinal-DG/CA3 circuitry retains item-specific information to allow fine discrimination of similar WM items across trials. The quality of item-specific WM information in the entorhinal-DG/CA3 pathway is associated with an observer’s subsequent recall fidelity. Together, these findings fill a missing link in the growing literature regarding the contribution of the MTL to item-level WM representation with a lower information load (Johnson et al., 2018; Sreenivasan and D’Esposito, 2019).

Theoretically, our findings are consistent with recent neural theories that highlight the involvement of distributed brain areas for WM (Christophel et al., 2017; Eriksson et al., 2015; Sreenivasan and D’Esposito, 2019), including mechanisms in the MTL that are traditionally deemed irrelevant for human WM (Beukers et al., 2021; Borders et al., 2022; Goodrich et al., 2019; Goodrich and Yonelinas, 2016). Our findings are built upon the established literature on the entorhinal-DG/CA3 circuitry and the formation of high-fidelity long-term episodic memory (Aimone et al., 2011; Bakker et al., 2008; Ekstrom and Yonelinas, 2020; Korkki et al., 2021; Leal and Yassa, 2018; Marr, 1971; Reagh and Yassa, 2014; Yassa and Stark, 2011). This function has been linked with various neuronal properties along the entorhinal-DG/CA3 pathway – such as abundant granule cells, strong inhibitory interneurons, and powerful mossy fiber synapses – which could enable sparse coding of information to minimize mnemonic interference (Aimone et al., 2011; Rolls, 2016; Rolls, 2013; Sahay et al., 2011). As such, similar information can be retained with a sufficient representational distance to support behavioral discrimination (Bakker et al., 2008; Burke et al., 2011; Hunsaker et al., 2008; Leal and Yassa, 2018; Leutgeb et al., 2007; Lohnas et al., 2018; Montchal et al., 2019; Reagh and Yassa, 2014; Ryan et al., 2012; Sakon and Suzuki, 2019). Our data suggest that the same MTL mechanism can also be used to support the quality of WM representation (Xie et al., 2020b). Conceptually, potential interference between items either across or within trials would place a demand on pattern separation even over a short delay (Oberauer and Lin, 2017). As such, the MTL circuitry involved in the resolution of mnemonic interference (Aimone et al., 2011) would play a key role in reducing inference between WM content and other similar information in the feature space. Our data suggest that this process would result in more similar and stable representations for the same remembered item across trials, as detected by multivariate correlational and decoding analyses. However, under certain task conditions (e.g. learning spatial routes in a naturalistic task over many repetitions), the MTL may maximally orthogonalize overlapping information to opposite representational patterns (hence ‘repulsion’) to minimize mnemonic interference (Chanales et al., 2017). It remains to be determined how these learning-related mechanisms in a more complex setting are related to MTL’s contributions to WM representation of simple stimulus features.

Empirically, our results have resolved an issue concerning the decodability of item-specific WM content in the MTL for simple stimulus features. Previously, MTL activity has been shown to scale with WM set size of letters and color squares without decodable item-specific WM content (Boran et al., 2022; Boran et al., 2019). When item information is shown, it often involves complex stimuli with rich information content (e.g. Kamiński et al., 2017). These observations raise the conceptual question concerning the extent to which the MTL responds to task difficulty or retains WM content. In other words, is the MTL not sensitive to simple stimuli or lower task demands at all? Here, with improved spatial resolution of MTL recordings and using a simple stimulus feature, our data suggest that the MTL retains item-level WM information even when the effective WM set size is one. The lack of significant observations in some previous studies using the same paradigm may be due to the lack of granularity in MTL recordings (e.g. Ester et al., 2015). To test this, we aggregated data from all the voxels in the hippocampus to examine whether blurred MTL signals would be sufficient to reveal item-specific WM content using the current IEM procedure. As CA1 and subiculum voxels contain less robust WM information (Figure 3B), we predicted that this aggregation procedure would attenuate the evidence for WM information due to the reduction in signal-to-noise ratio. Our data are in line with this prediction (Figure 3—figure supplement 4). These results, therefore, highlight the importance of fine-grained MTL signals in revealing item-specific WM content.

Alongside these theoretical and empirical contributions, our data also provide additional insights into the conditions under which the MTL is relevant for WM. First, our findings suggest that the MTL’s contribution to WM does not depend on whether task demands exceed a limited WM capacity (Jeneson and Squire, 2012), although this account has been proposed when interpreting some recent findings for WM tasks using complex stimuli or a higher memory set size (Boran et al., 2019; Jeneson and Squire, 2012; Kamiński et al., 2017; Kornblith et al., 2017; Libby et al., 2014). Second, our analysis has focused on the mid-delay activity (Postle et al., 2000) and hence our findings could not be explained by the MTL’s contribution to WM retrieval (Shrager et al., 2008). Furthermore, while our findings do not preclude the potential involvement of the MTL during perceptual encoding (Bonnen et al., 2021), perceptual involvement could not account for the results based on the comparison between the cued and uncued items (Bettencourt and Xu, 2016; Ester et al., 2015; Harrison and Tong, 2009). If the MTL primarily contributes to perceptual encoding instead of WM retention, we should have observed a comparable amount of information for both study items in the MTL, as they are presented in the same data acquisition TR before cue onset. Since participants do not know the cued item ahead of time, they need to initially remember both items. In line with this interpretation, a time-varying IEM analysis shows that aLEC-DG/CA3 indeed contains a comparable amount of information related to both the cued and uncued items at an earlier time point in the task (Figure 3—figure supplement 5). Yet, during the mid-delay period, aLEC-DG/CA3 contains significant information for the cued relative to the uncued item in a similar way as shown in the previous research (Ester et al., 2015; Harrison and Tong, 2009). Although it is well acknowledged that the current recording method has its inferential limitations in the time domain, these data unambiguously suggest that the entorhinal-DG/CA3 pathway supports the representation of a retrospectively selected memory item during a short delay – a hallmark of WM (Lorenc et al., 2021; Panichello and Buschman, 2021).

Several open questions remain to be addressed by future research. First, more data are needed to reveal how WM representation in the MTL is compared with and/or related to that retained in distributed neocortical areas (Christophel et al., 2017; Eriksson et al., 2015; Sreenivasan and D’Esposito, 2019). Although the IEM approach allows the reconstruction of information in neural signals, it is not well-suited to directly compare information reconstruction across brain regions. Such a comparison would be complicated by several issues, including the difference in the number of voxels involved and the lack of interpretability of null results when both brain regions contain some WM information. To improve interpretability, we have used the results based on the uncued item as a within-ROI control and contrasted how information specific to the cued item (cued vs. uncued) differs between MTL ROIs and a theoretically irrelevant control region (i.e. the amygdala). One additional potential approach is to examine how the representations of remembered items are correlated across brain regions (Figure 2—figure supplement 2A). The rationale is that delay-period neural patterns across trials should be correlated for two brain regions containing the same information (Pillet et al., 2019), as compared with brain regions that do not hold consistent information (Figure 2—figure supplement 2B). We tested this conjecture by examining the neural similarity across trials between the aLEC-DG/CA3 and a benchmark ROI in the superior temporal lobule (SPL) – a region that is consistently linked with item-specific information during visual WM retention both in the current data (Figure 3—figure supplement 4) and in the previous research (Bettencourt and Xu, 2016; Ester et al., 2015; Xu and Chun, 2006). Supporting this prediction, we found that the similarity of neural patterns between the aLEC-DG/CA3 and the SPL has increased from the pre-stimulus baseline to the WM retention period (Figure 2—figure supplement 2C), which contrasts with the lack of changes in the correlation of across-trial neural patterns between aLEC-DG/CA3 and the amygdala control ROI (Figure 2—figure supplement 2D). These data suggest that WM information in the entorhinal-DG/CA3 is similiar to that in a well-recognized neocortical WM-related area (Bettencourt and Xu, 2016; Ester et al., 2015; Xu and Chun, 2006). However, considering the limitation in temporal resolution of the current recording method, it remains unknown how the MTL contributes to the dynamic coding schemes underlying WM maintenance (Stokes, 2015). Future research with direct recordings from multiple brain areas would be more suitable to investigate the fine-scale temporal dynamic underlying these similar neural patterns across brain regions during WM.

Second, it remains unknown how the MTL circuitry is tuned to specific stimulus features such as orientations, although one of the analytical tools we used was inspired by findings based on neuronal tuning properties from the visual cortex (Brouwer and Heeger, 2009; Sprague et al., 2018). This is because the assumed orientation channels in IEM do not reflect the underlying neuronal tuning properties and are interpretable only within the assumed model (Liu et al., 2018; Sprague et al., 2018). Previous research using this method has therefore primarily focused on inferences related to the presence or absence of information content in the neural data (Bettencourt and Xu, 2016; Brouwer and Heeger, 2009; Ester et al., 2015; Rademaker et al., 2019; Sprague et al., 2016), instead of properties of neural tuning. In the current study, these IEM results are supported by the less assumption-laden results from stimulus-based representational similarity analysis (Kriegeskorte and Wei, 2021). These two approaches are therefore complementary to each other. Nevertheless, these analyses are correlational in nature. Hence, although fine-grained neural representations revealed by these analyses are associated with participants’ behavioral outcomes (Figure 4), it remains to be determined whether the entorhinal-DG/CA3 pathway contributes to the fidelity of WM representation or also to the process of information selection. Strategies for resolving this issue can involve generalizing the current findings to other WM tasks without an explicit requirement of retrospective information selection (Xie et al., 2023a) and/or further exploring how the frontal-parietal mechanisms related to visual selection and attention interact with the MTL system (Panichello and Buschman, 2021).

Third, the often-neglected role of the MTL in visual processing needs to be further explored. Our findings suggest that the entorhinal-DG/CA3 pathway in the MTL may play a role in retaining of task-relevant item-specific visual WM content, which could not be attributed to perceptual processing alone. These data adds to a growing body of literature that considers the MTL as an important part of the visual system, serving functions ranging from retinotopic coding (Knapen, 2021) to predictive coding (Hindy et al., 2016). Although retinotopic coding as a form of perceptual processing could underlie WM representation for orientation information, our data highlight that the MTL is sensitive to the retrospectively selected information – a hallmark of WM (Lorenc et al., 2021). Furthermore, while we have used orientation as a simple stimulus feature to minimize long-term memory influences, our results do not preclude the role of this MTL circuitry in remembering other stimulus features, such as colors (Xie et al., 2023a). To more precisely reveal the MTL mechanisms that are shared across WM and long-term memory, future research should examine the extent to which MTL voxels evoked by a long-term memory task (e.g., mnemonic similarity task, Bakker et al., 2008) can be directly used to directly decode mnemonic content in visual WM tasks using different simple stimulus features.

Non-identified data (e.g., MTL activities across ROIs and trial-by-trial behavior responses) and custom codes are available via the Open Science Framework repository (https://osf.io/zvdnr/).

1. 1. Xie W
   2. Cappiello M
   3. Yassa MA
   4. Ester E
   5. Zaghloul K
   6. Zhang W

   (2023) Open Science Framework

   ID zvdnr. The Entorhinal-DG/CA3 Pathway in the Medial Temporal Lobe Retains Visual Working Memory of a Simple Surface Feature.

   https://osf.io/zvdnr/

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   2. Deng W
   3. Gage FH

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Author details

1. Weizhen Xie

   1. Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Bethesda, United States
   2. Department of Psychology, University of California, Riverside, Riverside, United States
   3. Department of Psychology, University of Maryland, College Park, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   weizhen.xie@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4655-6496

2. Marcus Cappiello

   Department of Psychology, University of California, Riverside, Riverside, United States

   Contribution

   Data curation, Investigation, Writing – original draft, Project administration

   Competing interests

   No competing interests declared

3. Michael A Yassa

   Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8635-1498

4. Edward Ester

   Department of Psychology, University of Nevada, Reno, United States

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

5. Kareem A Zaghloul

   Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Bethesda, United States

   Contribution

   Resources, Formal analysis, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8575-3578

6. Weiwei Zhang

   Department of Psychology, University of California, Riverside, Riverside, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   weiwei.zhang@ucr.edu

   Competing interests

   No competing interests declared

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