A prevailing theory posits that spatial information travels from the postrhinal cortex (POR) to the medial entorhinal cortex (MEC), and nonspatial information is transmitted from the perirhinal cortex (PER) to the lateral entorhinal cortex (LEC), before both types of information merge in the hippocampus (Knierim et al., 2014). An important caveat of this theory is that it does not explain explicitly how visual scene information is processed in these circuits, although it becomes increasingly clear that visual scenes may provide critical contextual information to the hippocampus (Dombeck et al., 2010; Hassabis and Maguire, 2009; Kim et al., 2012; Maguire and Mullally, 2013; Prusky et al., 2004; Wirth et al., 2003; Zeidman et al., 2015).

Recently, we reported that the LEC and MEC can be functionally dissociated in scene-memory tasks (Yoo and Lee, 2017). Interestingly, we found that sensory cues interact with task-specific response types. Specifically, we found that the LEC, but not the MEC, is critical when rats were required to manipulate a common object in different ways (by pushing it or digging sand) using visual scenes in the background, whereas the MEC, but not the LEC, was important when rats made spatial choices in the T-maze using the same visual scenes.

Building on our prior findings, we here sought to address a critical question, namely, whether such dissociation uniquely occurs at the level of the EC or is inherited from its upstream structures. We tested this by pharmacologically manipulating the PER and POR, the direct upstream regions of the LEC and MEC, respectively, and testing rats in the same scene-based testing paradigms applied in the Yoo and Lee study. Furthermore, as a comparison to the null results reported in the LEC and MEC in object-based behavioral tasks in our previous study, we investigated whether the PER and POR play a role in such tasks (Yoo and Lee, 2017).

The following four different behavioral tasks, named based on stimulus and response types, were used in the current study: (i) scene-cued nonspatial response (SCN-NSR) task, (ii) scene-cued spatial response (SCN-SR) task, (iii) object-cued nonspatial response (OBJ-NSR) task, and (iv) object-cued spatial response (OBJ-SR) task. Rats (n = 23) trained to associate different types of stimuli (visual scenes and objects) with responses (spatial and nonspatial responses) were implanted bilaterally with cannulae in both the PER and POR (Figure 1A). Individual tip locations of cannulae were histologically verified (Figure 1B), and data from rats whose cannula-tip locations were misplaced from either area were discarded (n = 2).

Figure 1

Download asset Open asset

Similar contributions of the PER and POR to visual scene-based nonspatial responses

Rats (n = 7) were trained in the SCN-NSR task as described previously (Yoo and Lee, 2017) (Figure 2A). Injection of muscimol (MUS) into either the PER or POR (0.5 μL per site) resulted in significant performance deficits compared with injection of the same volume of artificial cerebrospinal fluid (ACSF) (Figure 2B; Figure 2—source data 1). A one-way repeated-measures ANOVA showed a significant effect of drug (F_(2,12) = 11.363, p=0.001). A Bonferroni-Dunn post hoc test (corrected α = 0.017) revealed significant differences in performance between ACSF and PER-MUS groups (p<0.01) and between ACSF and POR-MUS groups (p<0.001), but not between PER-MUS and POR-MUS groups (p>0.1) (Figure 2B). These results suggest that, unlike in the LEC and MEC (Yoo and Lee, 2017), both the PER and POR may play some role in nonspatial behavioral choices made using visual background scenes.

Figure 2

Download asset Open asset

Figure 2—source data 1

Performance in the SCN-NSR task.

Percent correct performance for individual rats under different drug conditions in the SCN-NSR task.

https://doi.org/10.7554/eLife.32657.004

Download elife-32657-fig2-data1-v2.xlsx

Figure 2—source data 2

Performance in the SCN-SR task.

Percent correct performance for individual rats under different drug conditions in the SCN-SR task.

https://doi.org/10.7554/eLife.32657.005

Download elife-32657-fig2-data2-v2.xlsx

Greater contribution of the PER than the POR to object-based memory tasks

In our previous study (Yoo and Lee, 2017), we reported that the LEC and MEC were not involved in the OBJ-NSR task. To test the involvement of the PER and POR in the same task, we trained the same rats (n = 7) used in the SCN-NSR task in the OBJ-NSR task (Figure 3A). Inactivation of the PER caused a noticeable drop in performance compared with controls (Figure 3B), whereas similar deficits were not found in the POR-MUS group. A one-way repeated-measures ANOVA showed a significant effect of drug (F_(2,12) = 15.548, p<0.001); a significant difference in performance was also found between ACSF and PER-MUS groups (p<0.001) and between PER-MUS and POR-MUS groups (p<0.001; Bonferroni-Dunn) (Figure 3B; Figure 3—source data 1). Interestingly, allowing rats to sample the object only visually in the same task recruited the PER similarly and, to some extent, the POR as well (see Figure 3—figure supplement 1, Figure 3—figure supplement 1—source data 1 for additional details).

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

Performance in the OBJ-NSR task.

Percent correct performance for individual rats under different drug conditions in the OBJ-NSR task.

https://doi.org/10.7554/eLife.32657.009

Download elife-32657-fig3-data1-v2.xlsx

Figure 3—source data 2

Performance in the OBJ-SR task.

Percent correct performance for individual rats under different drug conditions in the OBJ-SR task.

https://doi.org/10.7554/eLife.32657.010

Download elife-32657-fig3-data2-v2.xlsx

We further tested whether the PER was important when spatial choices were required upon recognizing an object, because the PER may be engaged in a task as long as an object is used as a cue, regardless of the response type. For this purpose, we trained another group of rats (n = 6) in the OBJ-SR task, in which rats learned to make spatial choices using a toy object as a cue (Figure 3C). Rats exhibited severe performance deficits in the PER-MUS group, as was the case in the OBJ-NSR task (Figure 3D; Figure 3—source data 2). Compared with the OBJ-NSR task, the POR-MUS group showed a trend toward mild deficits that failed to reach statistical significance. Specifically, a one-way repeated-measures ANOVA showed a significant effect of the drug (F_(2,10) = 7.611, p<0.01) and a significant difference only between ACSF and PER-MUS groups (p<0.005), but not between ACSF and POR-MUS groups (p=0.034, a value that did not reach the corrected α = 0.017 in Bonferroni-Dunn tests) (Figure 3D).

Functional dissociations are less clear in the upstream cortical regions of the EC

To compare the current findings with functional dissociations previously observed in the MEC and LEC (Yoo and Lee, 2017), we plotted the relative performance deficits under MUS conditions (compared to ASCF conditions) in all tasks by subtracting the performance level under MUS from that in control conditions (Figure 4A).

Figure 4 with 1 supplement see all

Download asset Open asset

In the NSR tasks (SCN-NSR and OBJ-NSR), the PER-MUS group showed large performance deficits in the OBJ-NSR task. The performance deficits in the SCN-NSR task were relatively smaller than those in the OBJ-NSR task. The POR-MUS group did not show any performance deficits in the OBJ-NSR task, but showed significant deficits in the SCN-NSR task (Figure 4A). A comparison of performance between OBJ-NSR and SCN-NSR tasks (two-way repeated measures ANOVA with task and region as within-subject factors) revealed a significant effect of region (F_(1,6) = 30.295, p<0.01), but not task (F_(1,6) = 0.08, p>0.5). The interaction between the two factors was significant (F_(1,6) = 6.561, p<0.05). A post hoc t-test (α = 0.025) showed a significantly larger deficit in the SCN-NSR than in the OBJ-NSR task in the POR (p<0.025), but not in the PER (p>0.1) (Figure 4A). Similar results were also obtained when only visual sampling was allowed in the OBJ-NSR task (Figure 4—figure supplement 1A).

In SR tasks (SCN-SR and OBJ-SR), the PER-MUS group showed larger deficits in the OBJ-SR task than in the SCN-SR task, whereas in the POR-MUS group, the results were similar between the two tasks (Figure 4B). There were no significant effects of region (F_(1,12) = 0.470, p=0.506) or task (F_(1,12) = 0.872, p=0.368), but the interaction between the two factors approached significance (F_(1,12) = 4.338, p=0.059).

Taken together, these findings strongly indicate that whether an object functions as a cue in a task may determine whether the PER plays an important role in the task, whereas the associated type of response may not be so critical. In contrast, the type of response (e.g., spatial navigational response) may play a greater role in recruiting the POR than the type of the cueing stimulus. An important finding in our study is that there is a condition in which the POR may not be necessary, namely the OBJ-NSR task (Figure 4A). One exception might be when a purely visual object is used as a cueing object (see below for further discussion). Most importantly, it is clear that the interaction between the stimulus attribute and response type between the PER and POR is not as strong as between the LEC and MEC (Figure 4C; Figure 4—figure supplement 1B and C).

In the current study, we sought to investigate functional differences between the PER and POR, the upstream regions of the LEC and MEC, respectively. Unlike in the LEC and MEC (Yoo and Lee, 2017), functional dissociations based on the response type (i.e., spatial vs. nonspatial) were less clear in the upstream areas in the scene-based tasks. Instead, the type of stimulus (i.e., object and scene) appears to critically determine the involvement of these areas in a task (Figure 4). Specifically, the PER was important in cases where an object stimulus should be recognized, but less so for scenes, regardless of the response type, whereas the POR was involved when either visual scene recognition or spatial navigation was necessary. Our findings suggest that the PER and POR may provide object and scene information to the LEC and MEC, respectively. In the EC, this information may be associated with task-demand–specific actions involving interaction with an object (LEC) or navigating in space (MEC). That is, the PER-LEC and POR-MEC networks may process ‘what should I do in relation to this object?’ and ‘where should I go from here?', respectively (Figure 4C).

Anatomically, the PER receives multimodal sensory inputs (e.g., auditory, visual, olfactory, and somatosensory) from various sensory-perceptual cortices, whereas the POR receives major inputs largely from visual areas, including the retrosplenial cortex (Agster and Burwell, 2009; Burwell and Amaral, 1998a). In the current study, inactivating the PER indeed produced large deficits compared with controls whenever rats were required to recognize an object before making a spatial or nonspatial response. In contrast, inactivation of the POR resulted in reliable deficits in performance whenever scene was used as a conditional cue before making a spatial or nonspatial choice. Together with prior findings (Epstein and Kanwisher, 1998; Murray and Richmond, 2001), our results suggest that the PER and POR may be specialized in recognizing (and perhaps perceiving) objects (Ahn and Lee, 2017; McTighe et al., 2010) and scenes, respectively.

We previously reported a strong double dissociation in the LEC and MEC between scene-based tasks that required nonspatial responses (i.e., object manipulation) and spatial responses (i.e., navigational turns) (Figure 4—figure supplement 1B) (Yoo and Lee, 2017). Testing rats in the same tasks with the PER or POR inactivated did not result in such strong dissociations in the current study (Figure 4—figure supplement 1C), suggesting that scene-response interactions may uniquely occur in the EC. However, when the stimulus category was extended to include objects, some interactions between stimulus and response types became apparent in the POR, but not necessarily in the PER. Specifically, a comparison of performance deficits between scene- and object-based tasks with the PER inactivated showed similarly larger deficits in object-based tasks (OBJ-NSR and OBJ-SR tasks) than in scene-based tasks (SCN-NSR and SCN-SR tasks), regardless of the response type. However, the navigational task demand in both OBJ-SR and SCN-SR tasks required the POR, irrespective of the type of stimulus, whereas the same region was only necessary when a visual scene (SCN-NSR task), but not an object (OBJ-NSR task), was used as a cue in nonspatial tasks. These results suggest that both scene recognition and spatial navigation constitute important computational components of the POR network (Figure 4C).

It is unclear why the navigational demand affected the POR regardless of the stimulus type, but had a lesser effect in the PER (Figure 4B). One possibility is that the POR indeed processes navigation-related signals in association with objects and scenes, based on the reciprocal connections with the MEC (Figure 4C). Because strong neural correlates for spatial navigation (e.g., grid cells and border cells) have been reported in the MEC (Hafting et al., 2005; Sargolini et al., 2006; Solstad et al., 2008), the MEC may bias the POR network toward processing incoming sensory inputs (e.g., visual object or scene information) in relation to the spatial navigation components of the task. Such a strong bias may not be exerted by the LEC to the PER, because object recognition may lead to many different types of responses in natural settings, as opposed to the case of scene recognition, which is normally associated with spatial navigation. Another possibility is that top-down influences from other navigation-related regions (e.g., retrosplenial cortex) might be stronger in the POR-MEC networks than in the PER-LEC networks. Considering the anatomical finding that the POR-to-PER connection is stronger than the PER-to-POR connection (Burwell and Amaral, 1998b; Furtak et al., 2007; Kealy and Commins, 2011), it is also possible that rats with inactivation in the POR might send disturbing signals to the PER, preventing normal object recognition in the OBJ-SR task (Figure 3D). Nonetheless, our study clearly demonstrates that the POR is not necessary if neither spatial navigation nor visual scene information processing is required.

Visual scenes used in our tasks may be considered as a context, and the PER-POR networks may play significant roles in contextual object recognition (Norman and Eacott, 2005; Heimer-McGinn et al., 2017). In natural situations, an animal never experiences an object completely detached from its background, and a visual context also always involves objects in it (Aminoff et al., 2013). Therefore, PER and POR networks may function in harmony in natural settings, rather than in isolation from each other. For example, an object is recognized efficiently when it appears against a contextually plausible background (Aminoff et al., 2013). Disrupting the POR network may affect such contextual object recognition (Furtak et al., 2012) in our SCN-NSR task because the ‘meaning’ (e.g., push or dig) of the object (i.e., jar) should be disambiguated using visual scenes in the background in that task. Likewise, inactivating the PER as in our study may make it harder for the animal to focus on the target object against its background context. These functional relationships between the PER and POR may underlie the similar deficits between PER-MUS and POR-MUS groups in the SCN-NSR task. Inactivation of the PER may also have some disruptive effects on the POR’s functions in scene information processing through PER-to-POR connections, leading to mild performance deficits (but still >80% correct) in the SCN-SR task in our study. Another possibility is that scene recognition in the POR may require some contribution of the PER, because a scene is normally composed of individual objects (e.g., individual pebble stones in the pebbles scene in our task). On a similar note, inactivation of the POR may make it difficult for an animal to focus on the cueing object in the OBJ-SR task, because contextual disruption, but not facilitation, of object recognition might occur.

In the view of traditional theory, spatial and nonspatial information is processed via separate streams in the medial temporal lobe (Knierim et al., 2014). According to this model, the physical attribute (i.e., spatial or nonspatial) of a stimulus determines the processing stream (i.e., PER-LEC or POR-MEC) to which the information is channeled. However, the current results, together with our previous findings, strongly suggest that such a view may be too simplistic. Our current working model is that the PER-LEC and POR-MEC networks are mainly concerned with object manipulation (or exploration) and spatial navigation (or contextual memory), respectively (Figure 4C). Our model posits that recognition of perceptual stimuli mainly occurs at the level of the PER (for objects) and POR (for scenes), and their task-related actions are associatively represented in the EC networks. The hippocampus, which receives these inputs together, may encode and retrieve various event memories in sequences as an animal navigates through a space to achieve goals.

1. 1. Agster KL
   2. Burwell RD

   (2009) Cortical efferents of the perirhinal, postrhinal, and entorhinal cortices of the rat

   Hippocampus 19:1159–1186.

   https://doi.org/10.1002/hipo.20578

   • PubMed
   • Google Scholar
2. 1. Ahn JR
   2. Lee I

   (2017) Neural Correlates of Both Perception and Memory for Objects in the Rodent Perirhinal Cortex

   Cerebral Cortex 27:3856–3868.

   https://doi.org/10.1093/cercor/bhx093

   • PubMed
   • Google Scholar
3. 1. Aminoff EM
   2. Kveraga K
   3. Bar M

   (2013) The role of the parahippocampal cortex in cognition

   Trends in Cognitive Sciences 17:379–390.

   https://doi.org/10.1016/j.tics.2013.06.009

   • PubMed
   • Google Scholar
4. 1. Burwell RD
   2. Amaral DG

   (1998a) Cortical afferents of the perirhinal, postrhinal, and entorhinal cortices of the rat

   The Journal of Comparative Neurology 398:179–205.

   https://doi.org/10.1002/(SICI)1096-9861(19980824)398:2<179::AID-CNE3>3.0.CO;2-Y

   • PubMed
   • Google Scholar
5. 1. Burwell RD
   2. Amaral DG

   (1998b) Perirhinal and postrhinal cortices of the rat: interconnectivity and connections with the entorhinal cortex

   The Journal of Comparative Neurology 391:293–321.

   https://doi.org/10.1002/(SICI)1096-9861(19980216)391:3<293::AID-CNE2>3.0.CO;2-X

   • PubMed
   • Google Scholar
6. 1. Canto CB
   2. Wouterlood FG
   3. Witter MP

   (2008) What Does the Anatomical Organization of the Entorhinal Cortex Tell Us?

   Neural Plasticity 2008:1–18.

   https://doi.org/10.1155/2008/381243

   • Google Scholar
7. 1. Dombeck DA
   2. Harvey CD
   3. Tian L
   4. Looger LL
   5. Tank DW

   (2010) Functional imaging of hippocampal place cells at cellular resolution during virtual navigation

   Nature Neuroscience 13:1433–1440.

   https://doi.org/10.1038/nn.2648

   • PubMed
   • Google Scholar
8. 1. Epstein R
   2. Kanwisher N

   (1998) A cortical representation of the local visual environment

   Nature 392:598–601.

   https://doi.org/10.1038/33402

   • PubMed
   • Google Scholar
9. 1. Furtak SC
   2. Ahmed OJ
   3. Burwell RD

   (2012) Single neuron activity and theta modulation in postrhinal cortex during visual object discrimination

   Neuron 76:976–988.

   https://doi.org/10.1016/j.neuron.2012.10.039

   • PubMed
   • Google Scholar
10. 1. Furtak SC
    2. Wei SM
    3. Agster KL
    4. Burwell RD

    (2007) Functional neuroanatomy of the parahippocampal region in the rat: the perirhinal and postrhinal cortices

    Hippocampus 17:709–722.

    https://doi.org/10.1002/hipo.20314

    • PubMed
    • Google Scholar
11. 1. Hafting T
    2. Fyhn M
    3. Molden S
    4. Moser MB
    5. Moser EI

    (2005) Microstructure of a spatial map in the entorhinal cortex

    Nature 436:801–806.

    https://doi.org/10.1038/nature03721

    • PubMed
    • Google Scholar
12. 1. Hassabis D
    2. Maguire EA

    (2009) The construction system of the brain

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:1263–1271.

    https://doi.org/10.1098/rstb.2008.0296

    • PubMed
    • Google Scholar
13. 1. Heimer-McGinn VR
    2. Poeta DL
    3. Aghi K
    4. Udawatta M
    5. Burwell RD

    (2017) Disconnection of the perirhinal and postrhinal cortices impairs recognition of objects in context but not contextual fear conditioning

    The Journal of Neuroscience 37:4819–4829.

    https://doi.org/10.1523/JNEUROSCI.0254-17.2017

    • PubMed
    • Google Scholar
14. 1. Kealy J
    2. Commins S

    (2011) The rat perirhinal cortex: A review of anatomy, physiology, plasticity, and function

    Progress in Neurobiology 93:522–548.

    https://doi.org/10.1016/j.pneurobio.2011.03.002

    • PubMed
    • Google Scholar
15. 1. Kim S
    2. Lee J
    3. Lee I

    (2012) The hippocampus is required for visually cued contextual response selection, but not for visual discrimination of contexts

    Frontiers in Behavioral Neuroscience 6:66.

    https://doi.org/10.3389/fnbeh.2012.00066

    • PubMed
    • Google Scholar
16. 1. Knierim JJ
    2. Neunuebel JP
    3. Deshmukh SS

    (2014) Functional correlates of the lateral and medial entorhinal cortex: objects, path integration and local-global reference frames

    Philosophical Transactions of the Royal Society B: Biological Sciences 369:20130369.

    https://doi.org/10.1098/rstb.2013.0369

    • PubMed
    • Google Scholar
17. 1. Maguire EA
    2. Mullally SL

    (2013) The hippocampus: a manifesto for change

    Journal of Experimental Psychology: General 142:1180–1189.

    https://doi.org/10.1037/a0033650

    • PubMed
    • Google Scholar
18. 1. McTighe SM
    2. Cowell RA
    3. Winters BD
    4. Bussey TJ
    5. Saksida LM

    (2010) Paradoxical false memory for objects after brain damage

    Science 330:1408–1410.

    https://doi.org/10.1126/science.1194780

    • PubMed
    • Google Scholar
19. 1. Murray EA
    2. Richmond BJ

    (2001) Role of perirhinal cortex in object perception, memory, and associations

    Current Opinion in Neurobiology 11:188–193.

    https://doi.org/10.1016/S0959-4388(00)00195-1

    • PubMed
    • Google Scholar
20. 1. Norman G
    2. Eacott MJ

    (2005) Dissociable effects of lesions to the perirhinal cortex and the postrhinal cortex on memory for context and objects in rats

    Behavioral Neuroscience 119:557–566.

    https://doi.org/10.1037/0735-7044.119.2.557

    • PubMed
    • Google Scholar
21. 1. Prusky GT
    2. Douglas RM
    3. Nelson L
    4. Shabanpoor A
    5. Sutherland RJ

    (2004) Visual memory task for rats reveals an essential role for hippocampus and perirhinal cortex

    PNAS 101:5064–5068.

    https://doi.org/10.1073/pnas.0308528101

    • PubMed
    • Google Scholar
22. 1. Sargolini F
    2. Fyhn M
    3. Hafting T
    4. McNaughton BL
    5. Witter MP
    6. Moser MB
    7. Moser EI

    (2006) Conjunctive representation of position, direction, and velocity in entorhinal cortex

    Science 312:758–762.

    https://doi.org/10.1126/science.1125572

    • PubMed
    • Google Scholar
23. 1. Solstad T
    2. Boccara CN
    3. Kropff E
    4. Moser MB
    5. Moser EI

    (2008) Representation of geometric borders in the entorhinal cortex

    Science 322:1865–1868.

    https://doi.org/10.1126/science.1166466

    • PubMed
    • Google Scholar
24. 1. Swanson LW
    2. Köhler C

    (1986)

    Anatomical evidence for direct projections from the entorhinal area to the entire cortical mantle in the rat.

    The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 6:3010–3033.

    • PubMed
    • Google Scholar
25. 1. Wirth S
    2. Yanike M
    3. Frank LM
    4. Smith AC
    5. Brown EN
    6. Suzuki WA

    (2003) Single neurons in the monkey hippocampus and learning of new associations

    Science 300:1578–1581.

    https://doi.org/10.1126/science.1084324

    • PubMed
    • Google Scholar
26. 1. Yoo SW
    2. Lee I

    (2017) Correction: Functional double dissociation within the entorhinal cortex for visual scene-dependent choice behavior

    eLife 6:e28407.

    https://doi.org/10.7554/eLife.28407

    • PubMed
    • Google Scholar
27. 1. Zeidman P
    2. Mullally SL
    3. Maguire EA

    (2015) Constructing, perceiving, and maintaining scenes: hippocampal activity and connectivity

    Cerebral Cortex 25:3836–3855.

    https://doi.org/10.1093/cercor/bhu266

    • PubMed
    • Google Scholar

Author details

1. Eun-Hye Park

   Department of Brain and Cognitive Sciences, Seoul National University, Shillim-dong, Korea

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing—original draft

   Contributed equally with

   Jae-Rong Ahn

   Competing interests

   No competing interests declared

2. Jae-Rong Ahn

   Department of Brain and Cognitive Sciences, Seoul National University, Shillim-dong, Korea

   Contribution

   Data curation, Software, Formal analysis, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Eun-Hye Park

   Competing interests

   No competing interests declared

3. Inah Lee

   Department of Brain and Cognitive Sciences, Seoul National University, Shillim-dong, Korea

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   inahlee@snu.ac.kr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3760-4257

• 2,257

  views

• 254

  downloads

• 12

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.