Entorhinal and hippocampal subregions form a critical functional circuitry that binds cortical information into cohesive representations (Eichenbaum et al., 2007; Ritchey et al., 2015). The interaction of the entorhinal-hippocampal circuitry with large-scale cortical information streams and the circuitry’s inner communication are key to the formation of these cohesive representations. Here, we advance insight into how the human entorhinal cortex (EC) receives information from cortical streams and how information proceeds between the EC and the transversal axis of hippocampal subiculum and CA1 (here referred to as transversal sub/CA1 axis). These insights are relevant to our understanding of the circuitry’s fundamental role in cognitive functions such as episodic memory.

Large-scale cortical information streams, that originate in the visual ‘Where’ and ‘What’ pathways and process scene and object information (Berron et al., 2018; Haxby et al., 1991; Ranganath and Ritchey, 2012; Ritchey et al., 2015; Ungerleider and Haxby, 1994), map onto the EC in a complex manner and define functional EC subregions. [Note, in light of confusing nomenclature, here we adhere to scene and object information - elsewhere referred to as contextual, spatial or "Where" and content, non-spatial, item or "What" information, respectively.] Recent rodent research updates the former conception of a parallel mapping of scene and object information via parahippocampal and perirhinal cortices onto medial versus lateral EC subregions (cf. posterior-medial versus anterior-lateral EC subregions as the human homologues; Maass et al., 2015; Navarro Schröder et al., 2015). Instead of a strict parallel mapping, profound cross-projections exist from the parahippocampal cortex towards the perirhinal cortex and the lateral EC (Nilssen et al., 2019). In accordance, information seems to converge in the rodent lateral EC (Doan et al., 2019). The update, thus, implies a more complex functional organization than parallel scene and object information mapping. Moreover, this advance highlights the retrosplenial cortex as an additional source to convey information directly from the cortical scene processing stream onto the EC. The retrosplenial cortex projects to the medial EC and, like the parahippocampal cortex, is part of the scene processing stream (e.g. involved in scene translation; Vann et al., 2009; Nilssen et al., 2019; Witter et al., 2017). The update, furthermore, evokes the question how cortical sources of information uniquely map onto the EC and which kind of information is processed in the resulting functional EC subregions.

Within the entorhinal-hippocampal circuitry, an important direct way of communication exists between the EC and hippocampal subiculum and CA1. How functional EC subregions communicate towards the transversal sub/CA1 axis in humans is, however, unclear. Similarly, the extent to which specific scene and object information processing routes might emerge, despite information convergence in the EC, is unknown. On one hand, rodent research indicates a transversal organization where scene and object information is processed along two anatomically wired routes, the medial EC – distal subiculum – proximal CA1 route and the lateral EC – proximal subiculum – distal CA1 route, respectively (Witter et al., 2017; note sparse functional evidence in the subiculum: Ku et al., 2017; Cembrowski et al., 2018; but frequent reports in the rodent CA1 region: Henriksen et al., 2010; Nakamura et al., 2013; Igarashi et al., 2014; Nakazawa et al., 2016; Beer et al., 2018). Initial functional and structural connectivity data also indicate such a transversal connectivity profile in humans (Maass et al., 2015; Syversen et al., 2021). In accordance, scene information seems to be preferentially processed in the distal subiculum (Dalton et al., 2018; Dalton and Maguire, 2017; Zeidman et al., 2015) and hints exist for preferential object processing at the subiculum/CA1 border (Dalton et al., 2018). On the other hand, anatomical projections in the monkey show a longitudinal profile on top of the transversal profile with mainly the anterior-lateral and posterior-lateral entorhinal portions projecting to the distal subiculum – proximal CA1 and proximal subiculum – distal CA1, respectively (Witter and Amaral, 2020). According to information convergence in the EC, a recent report finds convergence along the rodent transversal CA1 axis (Vandrey et al., 2021). In humans, visual stream projections towards the entorhinal-hippocampal circuitry similarly suggest convergence of scene and object information in the subiculum/CA1 border region but preserved scene processing in the distal subiculum (Dalton and Maguire, 2017). A detailed examination of the latter hypothesis is, however, lacking. The diversity of findings emerging from the literature calls for a thorough investigation to elucidate whether multiple transversal processing routes exist within the human entorhinal-hippocampal circuitry.

To summarize, our conception of how information travels towards the entorhinal-hippocampal circuitry underwent key changes which warrant an extensive exploration of the circuitry’s functional organization. First, rodent research shows that there is no strict parallel mapping of cortical information from the perirhinal and parahippocampal cortex towards the EC. Second, information seems to converge already before the hippocampus. These changes add to several knowledge gaps. First, it is unclear in which subregions of the entorhinal-hippocampal circuitry scene and object information are processed. The general connectivity patterns in the human entorhinal-hippocampal circuitry have not yet been directly related to information processing. Moreover, it is unclear how scene information from the retrosplenial cortex maps onto the human EC as a critical source of the cortical scene processing stream. Hence, it is also unclear how retrosplenial information is communicated between the EC and the hippocampus. Finally, it remains elusive whether a transversal functional segregation can be extended towards the human CA1 region in analogy to the rodent literature.

Here, we leverage ultra-high field 7 Tesla functional imaging (fMRI) data and advance the earlier findings on human entorhinal subregions and a transversal intrinsic functional connectivity pattern in the subiculum (Maass et al., 2015). With a combination of functional connectivity and information processing analyses, we seek to answer two sets of questions. Regarding functional connectivity, we ask where the parahippocampal, perirhinal, and retrosplenial cortical sources uniquely map onto the human EC and how these functionally connected routes continue between EC subregions and the transversal sub/CA1 axis. Regarding information processing, we ask whether and where scene and object information are specifically processed in the EC and along the transversal sub/CA1 axis. We test the hypotheses of (1) a transversal functional connectivity pattern and (2) multiple information processing routes within the entorhinal-hippocampal circuitry. Thus, following the updated conception of a non-parallel cortical scene and object information mapping onto the EC in rodents, we will show how cortical information streams map onto the EC in humans. This mapping will then be our detailed starting point to investigate the functional connectivity and information processing within the entorhinal-hippocampal circuitry.

We seek to comprehensively investigate functional connectivity within the entorhinal-hippocampal circuitry and the contribution of cortical scene and object information processing streams. In an initial step, we identified where cortical sources map uniquely onto the entorhinal cortex (building upon Maass et al., 2015). The identified entorhinal subregions are based on their voxel’s preferred intrinsic functional connectivity with the retrosplenial cortex, parahippocampal cortex, perirhinal Area 35 or Area 36 regions (‘sources’). Note, that we evaluate both perirhinal subregions, Area 35 and Area 36 as separate sources as accumulating evidence suggests their structural and functional distinction (e.g. Berron et al., 2021; Burwell, 2000; Suzuki and Naya, 2014; van Strien et al., 2009). Next, we evaluated the continuation of the functional connectivity streams within the entorhinal-hippocampal circuitry and examined the intrinsic functional connectivity pattern between the identified entorhinal subregions (‘seeds’) and hippocampal subiculum and CA1 in the hippocampal body. Therefore, temporal fluctuations of BOLD signal were correlated in a seed-to-voxel manner within each participant. The resulting statistical correlational maps were aligned between participants. Repeated measures ANOVAs were calculated on connectivity preferences with seeds and transversal segments as factors to determine statistical differences in connectivity topography. All functional connectivity analyses were performed on the dataset after task-related effects have been regressed out, creating a dataset that resembles resting-state data (Gavrilescu et al., 2008; Maass et al., 2015).

Finally, we identified the corresponding bias in scene (here operationalized with room stimuli) and object information processing within entorhinal subregions and along the transversal sub/CA1 axis using the same dataset. Therefore, we extracted parameter estimates from a mnemonic discrimination task with scene and object conditions from aligned statistical maps across participants. Repeated measures ANOVAs were calculated on parameter estimates in entorhinal subregions and transversal sub/CA1 segments to determine biases in information processing within the entorhinal-hippocampal circuitry.

In the following, we first describe the four obtained entorhinal seeds and display the intrinsic functional connectivity pattern with the entorhinal seed regions along the transversal sub/CA1 axis. Thereafter, we report the information processing characteristics of the entorhinal and hippocampal subregions. Note that all results have been obtained with independent analyses in the left and right hemispheres. The largely similar left hemisphere results can be found in appendix 1. Source data and statistical maps are provided under Grande, X., Berron, D. (2022). Open Science Framework. ID 9v3qp. Source Data from Functional Connectivity and Information Processing in the Entorhinal-Hippocampal Circuitry. https://osf.io/9v3qp.

Four cortical sources divide the EC in retrosplenial-, parahippocampal, Area 35- and Area 36-based seeds

The four entorhinal subregions that we later used as seeds to determine the topography of entorhinal-hippocampal connectivity are based on intrinsic functional connectivity preferences with either the parahippocampal cortex, the retrosplenial cortex, perirhinal Area 36 or Area 35. These cortical regions are in general concordance with Maass et al., 2015 but consider recent advances that put forward the retrosplenial cortex as a critical source from the cortical scene processing stream (Nilssen et al., 2019) and evaluate perirhinal Area 35 and 36 separately.

Based on functional connectivity preferences with the four sources - parahippocampal cortex (Source code 8), retrosplenial cortex (Source code 7, Area 36 (Source code 6), and Area 35 (Source code 5) - we obtained four entorhinal seeds. The seeds refer to different parts of the EC whose voxels expressed preferential functional connectivity to either cortical source. For the EC_PHC-based seed, the majority of voxels can roughly be described as clustering in the posterior-medial entorhinal portion, for the EC_RSC-based seed in the anterior-medial portion, for the EC_Area35-based seed in the anterior-lateral portion and for the EC_Area36-based seed in the posterior-lateral entorhinal portion (see appendix 2 for exact voxel counts). Note that both perirhinal-based entorhinal seeds extended along the anterior to posterior axis such that the EC_Area35-based progressed more along the outer EC (i.e. laterally, with a main focus anteriorly) and the EC_Area36-based along the inner EC (i.e. medially, with a main focus posteriorly, see Figure 1 and the medial reflection of the EC seeds). It is important to note that these are rough qualitative descriptions of the main clusters, without quantification or an established relationship to coherent cytoarchitectonic regions. We will therefore continue to refer to them as EC_RSC-based, EC_PHC-based, EC_Area35-based and EC_Area36-based seeds.

Figure 1

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Distal subiculum is functionally connected with the EC_PHC-based seed while the subiculum/CA1 border is connected with EC_RSC-based and EC_Area35-based seeds

Following the characterization of entorhinal seeds, we focused on the functional connectivity between these entorhinal subregions and hippocampal subiculum and CA1 to test the hypothesis of a transversal functional connectivity pattern. We predicted that while some EC subregions have a preference to functionally connect with the subiculum/CA1 border, others preferentially connect with the distal subiculum and proximal CA1. In the previous step, we identified EC subregions based on unique cortical source contributions. Therefore, our predictions remained in accordance with Maass et al., 2015: We expected that the EC subregion preferentially connected with the parahippocampal cortex (EC_PHC-based seed) maps towards the distal subiculum and EC subregions connected with the perirhinal cortex (EC_Area35-based seed, EC_Area36-based seed) map towards the proximal subiculum, a mapping that we predicted to be extended towards the distal CA1.

When extracting estimates of connectivity preferences across individuals from proximal and distal hippocampal subfield segments for either entorhinal seed, repeated measures ANOVAs revealed significant seed X segments interaction effects along the transversal sub/CA1 axis (see Figure 2; subiculum: F(12,372) = 19.561; p<0.001; CA1: F(6,186) = 3.212; p=0.024).

Figure 2

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Figure 2—source data 1

Individual functional connectivity estimates to right entorhinal seeds, extracted from right subiculum and CA1 transversal segments.

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In the subiculum, additional repeated measures ANOVAs showed that the EC_Area35-based (F(4,124) = 8.913; p_FDR <0.001), EC_RSC-based (F(4,124) = 10.538; p_FDR <0.001) and EC_PHC-based (F(4,124) = 42.201; p_FDR <0.001) seeds displayed a significant main effect across the transversal subiculum segments. These differential functional connectivity preferences across the transversal axis of the subiculum interacted significantly in a subsequent repeated measures ANOVA (EC_PHC-based versus EC_RSC-based seed preference interaction: F(4,124) = 46.452; p_FDR <0.001; EC_PHC-based versus EC_Area35-based seed preference interaction: F(4,124) = 35.208; p_FDR <0.001). This pattern provides statistical evidence for an increase in preferential functional connectivity with the EC_PHC-based seed towards the distal portion of the subiculum while the preferential functional connectivity with the EC_Area35-based as well as the EC_RSC-based seeds rather increased towards the proximal portion of the subiculum.

In hippocampal CA1, additional repeated measures ANOVAs showed that the connectivity preference towards the EC_RSC-based seed displays a significant main effect across the transversal axis of CA1 (F(2,62) = 10.489; p_FDR <0.001). In distal CA1, the preferential functional connectivity with the EC_RSC-based seed was higher than in the proximal portion of CA1. In right CA1, a similar but weaker transversal pattern was observed for connectivity preferences with the EC_Area35-based (F(2,62) = 4.146; p_FDR = 0.041; note in the left hemisphere a comparable transversal pattern was observed for the EC_PHC-based and EC_RSC-based portions, see appendix 1).

Thus, in the entorhinal-hippocampal circuitry, voxels in the distal subiculum were preferentially functionally connected with the EC_PHC-based portion whereas voxels in the subiculum/CA1 border were preferentially connected with more anterior EC portions (EC_RSC-based and EC_Area35-based).

Distal subiculum and EC_PHC-based exhibit higher functional activity in the scene condition while other subregions show no significant difference between conditions

Besides the intrinsic functional connectivity patterns within the entorhinal-hippocampal circuitry, we also examined the characteristics of scene and object information processing to test the hypothesis of multiple information processing routes within the entorhinal-hippocampal circuitry. We predicted a route of specific scene processing and another route of convergent information processing. Following the proposal by Dalton and Maguire, 2017 and the updated cross-projections from the scene to the object information processing stream (Nilssen et al., 2019), we expected scene processing in the distal subiculum. The updated parahippocampal cross-projections imply convergence wherever specific object processing had been expected previously. Thus, we explored whether any entorhinal-hippocampal subregions still process object information specifically. However, we largely expected to find evidence consistent with convergent processing of scene and object information within the entorhinal-hippocampal circuitry.

We first focused on the entorhinal seed regions. When extracting task-related parameter estimates from object and scene conditions, a repeated measures ANOVA showed a significant interaction between region and information type (object versus scene, F(3,93) = 20.9267; p<0.001). Post-hoc t-tests revealed that only in the EC_PHC-based seed region functional activity in the scene condition was significantly higher than in the object condition (p_FDR<0.001), while in the remaining three entorhinal seed regions no significant difference between scene and object conditions existed (EC_Area35-based: p_FDR = 0.9129; EC_Area36-based: p_FDR = 0.9129; EC_RSC-based: p_FDR = 0.5646; see Figure 3).

Figure 3

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Figure 3—source data 1

Individual paramenter estimates for scene and object processing in right entorhinal seed regions.

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When extracting task-related parameter estimates for scene and object conditions from proximal and distal segments of hippocampal subregions within each participant, we found a significant interaction between transversal segments and information type only in the subiculum (F(4,124) = 15.994; p<0.001) and not in CA1 (F(2,62) = 2.553; p = 0.105) as revealed by repeated measures ANOVAs. Post-hoc T-tests showed significantly higher functional activity in the scene than object condition (both p_FDR <0.001) only in the distal subiculum segments. In all other segments along the subiculum transversal axis, there was no significant difference in functional activity related to scene and object conditions (all p_FDR = 0.1222; see Figure 4).

Figure 4

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Figure 4—source data 1

Individual parameter estimates for scene and object processing in right transversal subiculum and CA1 segments.

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This study aims to advance insight into the organizational principles of information processing within the entorhinal-hippocampal circuitry and the circuitry’s embedding in designated cortical processing streams. Leveraging ultra-high field 7 Tesla fMRI, we find a resemblance between the intrinsic functional connectivity pattern and subregional biases in scene information processing in the entorhinal-hippocampal circuitry. In the EC, we observe a topographical mapping of regions from the cortical scene and object information processing streams, including the retrosplenial, parahippocampal and perirhinal Area 35 and Area 36 cortices. This mapping continues to determine a transversal organization of information processing routes between the EC and the human hippocampal circuitry. Our results unify previous evidence and uncover novel features in the human brain that can be a window into the circuitry’s critical role in memory function and decline.

Relevance of the current findings for the functional organization of the entorhinal-hippocampal circuitry

The current findings (summarized in Figure 5) advance our insight into the organization of the entorhinal-hippocampal circuitry on multiple levels and contribute to cognitive and clinical research. Recent efforts to understand how the human entorhinal-hippocampal circuitry accomplishes conjunction and segregation of information largely focused on the longitudinal hippocampal axis (e.g. Brunec et al., 2018; Brunec et al., 2020; Robin and Moscovitch, 2017). The transversal axis of the hippocampus has been approached by studies in humans that did not directly relate connectivity findings to information processing and did not assess subfield-specific organization (Vos de Wael et al., 2018; Plachti et al., 2019; Kharabian Masouleh et al., 2020; Paquola et al., 2020; Bouffard et al., 2022; for an overview see Genon et al., 2021). Dalton and Maguire, 2017, however, made a relevant proposal based on visual processing pathways and information processing. In correspondence to our results, they proposed the subiculum/CA1 border as a point of convergence between scene and object information processing streams. While their conclusion was based on direct parahippocampal, retrosplenial and perirhinal connections to the hippocampus, we found that both, the EC_Area35-based (that is connected with the cortical object processing stream) and the EC_RSC-based (that is connected with the cortical scene processing stream) show connectivity with the subiculum/CA1 border (see also appendix V for information processing in cortical source regions). Convergence is potentially also achieved via recurrency within the entorhinal-hippocampal system and cortical regions (cf. Koster et al., 2018 for evidence on recurrency). These considerations are an exciting future research avenue and remain speculative based on the current data due to insufficient temporal resolution. We nevertheless hypothesize the existence of two processing routes: one that processes converged object and scene information and one that processes scene information specifically. Thus, scene and object information processing might converge before the hippocampus. This presumably occurs within the anterior EC, given object-specific and scene-specific processing take place in the cortical source regions of the EC_Area35-based and EC_RSC-based portions, respectively (see appendix 5). Here, objects may be bound together with their defining scene-like or contextual features (akin to the ‘object-in-location’ idea in Connor and Knierim, 2017; Knierim et al., 2014). In addition, the dedicated scene processing that we observe along the EC_PHC-based – distal subiculum route, may functionally underpin ideas about an anatomically graded contextual scaffold that the hippocampus utilizes to incorporate detailed information from the object-in-scene route into meaningful chunks of cohesive memory representations (‘events’; Behrens et al., 2018; Clewett et al., 2019; Robin, 2018; Robin and Olsen, 2019).

Figure 5

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For completeness, we noted differences in functional connectivity along the longitudinal axis of the subiculum. We observed, for instance, more widespread functional connectivity of the EC_Area35-based in the posterior subiculum whereas functional connectivity with the EC_PHC-based portion seems more prominent in the anterior subiculum. The latter is consistent with previous reports (Dalton et al., 2019). The former, however, needs to be explored further by taking different segmentation protocols and seed regions into account. Note, that Maass et al., 2015 did not report longitudinal differences in connectivity strength between the EC and the subiculum. Future work needs to investigate how these observations relate to the reported gradient in functional connectivity and information resolution along the hippocampal longitudinal axis (e.g. Brunec et al., 2018 but many more). Altogether, the functional organization indicates that when a memory is to be formed, some degree of information convergence happens already before the hippocampus, nevertheless keeping specific aspects of scene information separated. This conclusion is in accordance with the updated cortical mapping scheme onto the EC (Nilssen et al., 2019). The topographical specificity of our results supports the necessity of functionally assessing the entorhinal-hippocampal circuitry with high spatial resolution and investigate memory function at a subregional level (Lee et al., 2020). The features we identified can inform future hypotheses on how the hippocampus achieves the formation of cohesive representations that serve memory function.

From a clinical research perspective, it is remarkable that the current functional connectivity pattern resembles the topology of early cortical tau pathology in Alzheimer’s disease (Lace et al., 2009). An influential hypothesis suggests tau progression in Alzheimer’s disease along functionally connected pathways in the human brain (Franzmeier et al., 2020; Vogel et al., 2020). Earliest cortical tau pathology in Alzheimer’s disease accumulates in perirhinal Area 35 (also referred to as transentorhinal region) and the anterior-lateral EC before it can be found along the subiculum/CA1 border (Braak and Braak, 1995; Berron et al., 2021; Kaufman et al., 2018; Lace et al., 2009). The topology of early tau pathology in Alzheimer’s disease thus mirrors the regions that we find biased towards EC_Area35-based connectivity (Braak and Braak, 1991; Lace et al., 2009; Roussarie et al., 2020). Tau pathology in Alzheimer’s disease is associated with memory impairment (Bejanin et al., 2017; Berron et al., 2021; Nelson et al., 2012) and information processing might be affected accordingly as reports have shown an association between Alzheimer’s related tau pathology and object memory in early disease stages (Berron et al., 2019; Maass et al., 2019). However, given our finding of activity patterns consistent with object – scene convergence in those subregions of the hippocampal-entorhinal circuitry that are affected by early tau pathology, object-in-scene memory tasks might have increased sensitivity to memory impairment. Moreover, both, the entorhinal portion based on retrosplenial connectivity (EC_RSC-based) and the entorhinal portion based on Area 35 connectivity (EC_Area35-based), are functionally connected to the subiculum/CA1 border. This overlapping functional connectivity pattern in the hippocampus might be a way along which tau and amyloid pathologies in Alzheimer’s disease could interact. This is consistent with early hypometabolism and cortical tau progression in the retrosplenial cortex and early amyloid in posterior parietal regions (Grothe et al., 2017; Palmqvist et al., 2017; Ziontz et al., 2021). The revealed functional connectivity and information processing profile may guide future hypotheses on the propagation of Alzheimer’s pathology and related functional and cognitive impairment.

The current data is part of a larger study that examines exercise effects on cognition. The data that is subject to the current study have been acquired during the baseline measurement before any intervention took place. In the following, we focus on the study setup and methodological aspects of direct relevance for the current questions and data analyses.

Source data that contain numerical data used to generate Figure 2, Figure 3, Figure 4, Appendix 1 Figure 2, Appendix 1 Figure 3, Appendix 1 Figure 4, Appendix 5 Figure 1 as well as group-level statistical maps (referred to as Source Code 1-16) that underlie Figure 1, Figure 2, Appendix 1 Figure 1, Appendix 1 Figure 2, Appendix 3 Figure 1, Appendix 3 Figure 2 and Appendix 4 Figure 1 have been provided under: Open Science Framework. ID 9v3qp. https://osf.io/9v3qp.

1. 1. Grande X
   2. Berron D

   (2022) Open Science Framework

   ID 9v3qp. Source Data from Functional Connectivity and Information Processing in the Entorhinal-Hippocampal Circuitry.

   https://osf.io/9v3qp

1. 1. Avants BB
   2. Yushkevich P
   3. Pluta J
   4. Minkoff D
   5. Korczykowski M
   6. Detre J
   7. Gee JC

   (2010) The optimal template effect in hippocampus studies of diseased populations

   NeuroImage 49:2457–2466.

   https://doi.org/10.1016/j.neuroimage.2009.09.062

   • PubMed
   • Google Scholar
2. 1. Avants BB
   2. Tustison NJ
   3. Song G
   4. Cook PA
   5. Klein A
   6. Gee JC

   (2011) A reproducible evaluation of ants similarity metric performance in brain image registration

   NeuroImage 54:2033–2044.

   https://doi.org/10.1016/j.neuroimage.2010.09.025

   • PubMed
   • Google Scholar
3. 1. Basti A
   2. Nili H
   3. Hauk O
   4. Marzetti L
   5. Henson RN

   (2020) Multi-dimensional connectivity: a conceptual and mathematical review

   NeuroImage 221:117179.

   https://doi.org/10.1016/j.neuroimage.2020.117179

   • PubMed
   • Google Scholar
4. 1. Beer Z
   2. Vavra P
   3. Atucha E
   4. Rentzing K
   5. Heinze HJ
   6. Sauvage MM

   (2018) The memory for time and space differentially engages the proximal and distal parts of the hippocampal subfields CA1 and CA3

   PLOS Biology 16:e2006100.

   https://doi.org/10.1371/journal.pbio.2006100

   • PubMed
   • Google Scholar
5. 1. Behrens TEJ
   2. Muller TH
   3. Whittington JCR
   4. Mark S
   5. Baram AB
   6. Stachenfeld KL
   7. Kurth-Nelson Z

   (2018) What is a cognitive map? organizing knowledge for flexible behavior

   Neuron 100:490–509.

   https://doi.org/10.1016/j.neuron.2018.10.002

   • PubMed
   • Google Scholar
6. 1. Bejanin A
   2. Schonhaut DR
   3. La Joie R
   4. Kramer JH
   5. Baker SL
   6. Sosa N
   7. Ayakta N
   8. Cantwell A
   9. Janabi M
   10. Lauriola M
   11. O’Neil JP
   12. Gorno-Tempini ML
   13. Miller ZA
   14. Rosen HJ
   15. Miller BL
   16. Jagust WJ
   17. Rabinovici GD

   (2017) Tau pathology and neurodegeneration contribute to cognitive impairment in alzheimer’s disease

   Brain 140:3286–3300.

   https://doi.org/10.1093/brain/awx243

   • PubMed
   • Google Scholar
7. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
8. 1. Berron D
   2. Vieweg P
   3. Hochkeppler A
   4. Pluta JB
   5. Ding SL
   6. Maass A
   7. Luther A
   8. Xie L
   9. Das SR
   10. Wolk DA
   11. Wolbers T
   12. Yushkevich PA
   13. Düzel E
   14. Wisse LEM

   (2017) A protocol for manual segmentation of medial temporal lobe subregions in 7 tesla MRI

   NeuroImage. Clinical 15:466–482.

   https://doi.org/10.1016/j.nicl.2017.05.022

   • PubMed
   • Google Scholar
9. 1. Berron D
   2. Neumann K
   3. Maass A
   4. Schütze H
   5. Fliessbach K
   6. Kiven V
   7. Jessen F
   8. Sauvage M
   9. Kumaran D
   10. Düzel E

   (2018) Age-related functional changes in domain-specific medial temporal lobe pathways

   Neurobiology of Aging 65:86–97.

   https://doi.org/10.1016/j.neurobiolaging.2017.12.030

   • PubMed
   • Google Scholar
10. 1. Berron D
    2. Cardenas-Blanco A
    3. Bittner D
    4. Metzger CD
    5. Spottke A
    6. Heneka MT
    7. Fliessbach K
    8. Schneider A
    9. Teipel SJ
    10. Wagner M
    11. Speck O
    12. Jessen F
    13. Düzel E

    (2019) Higher CSF tau levels are related to hippocampal hyperactivity and object mnemonic discrimination in older adults

    The Journal of Neuroscience 39:8788–8797.

    https://doi.org/10.1523/JNEUROSCI.1279-19.2019

    • PubMed
    • Google Scholar
11. 1. Berron D
    2. Vogel JW
    3. Insel PS
    4. Pereira JB
    5. Xie L
    6. Wisse LEM
    7. Yushkevich PA
    8. Palmqvist S
    9. Mattsson-Carlgren N
    10. Stomrud E
    11. Smith R
    12. Strandberg O
    13. Hansson O

    (2021) Early stages of tau pathology and its associations with functional connectivity, atrophy and memory

    Brain 144:2771–2783.

    https://doi.org/10.1093/brain/awab114

    • PubMed
    • Google Scholar
12. Preprint

    1. Bouffard NR
    2. Golestani A
    3. Brunec IK
    4. Bellana B
    5. Park JY
    6. Barense MD
    7. Moscovitch M

    (2022) Single Voxel Autocorrelation Uncovers Gradients of Temporal Dynamics in the Hippocampus and Entorhinal Cortex during Rest and Navigation

    bioRxiv.

    https://doi.org/10.1101/2021.07.28.454036

    • Google Scholar
13. 1. Braak H
    2. Braak E

    (1991) Neuropathological stageing of alzheimer-related changes

    Acta Neuropathologica 82:239–259.

    https://doi.org/10.1007/BF00308809

    • PubMed
    • Google Scholar
14. 1. Braak H
    2. Braak E

    (1995) Staging of alzheimer’s disease-related neurofibrillary changes

    Neurobiology of Aging 16:271–278.

    https://doi.org/10.1016/0197-4580(95)00021-6

    • PubMed
    • Google Scholar
15. 1. Brunec IK
    2. Bellana B
    3. Ozubko JD
    4. Man V
    5. Robin J
    6. Liu ZXX
    7. Grady C
    8. Rosenbaum RS
    9. Winocur G
    10. Barense MD
    11. Moscovitch M

    (2018) Multiple scales of representation along the hippocampal anteroposterior axis in humans

    Current Biology 28:2129–2135.

    https://doi.org/10.1016/j.cub.2018.05.016

    • PubMed
    • Google Scholar
16. 1. Brunec IK
    2. Robin J
    3. Olsen RK
    4. Moscovitch M
    5. Barense MD

    (2020) Integration and differentiation of hippocampal memory traces

    Neuroscience and Biobehavioral Reviews 118:196–208.

    https://doi.org/10.1016/j.neubiorev.2020.07.024

    • PubMed
    • Google Scholar
17. 1. Burwell RD

    (2000) The parahippocampal region: corticocortical connectivity

    Annals of the New York Academy of Sciences 911:25–42.

    https://doi.org/10.1111/j.1749-6632.2000.tb06717.x

    • PubMed
    • Google Scholar
18. 1. Cembrowski MS
    2. Phillips MG
    3. DiLisio SF
    4. Shields BC
    5. Winnubst J
    6. Chandrashekar J
    7. Bas E
    8. Spruston N

    (2018) Dissociable structural and functional hippocampal outputs via distinct subiculum cell classes

    Cell 173:1280–1292.

    https://doi.org/10.1016/j.cell.2018.03.031

    • PubMed
    • Google Scholar
19. 1. Clewett D
    2. DuBrow S
    3. Davachi L

    (2019) Transcending time in the brain: how event memories are constructed from experience

    Hippocampus 29:162–183.

    https://doi.org/10.1002/hipo.23074

    • PubMed
    • Google Scholar
20. 1. Connor CE
    2. Knierim JJ

    (2017) Integration of objects and space in perception and memory

    Nature Neuroscience 20:1493–1503.

    https://doi.org/10.1038/nn.4657

    • PubMed
    • Google Scholar
21. 1. Czajkowski R
    2. Sugar J
    3. Zhang SJ
    4. Couey JJ
    5. Ye J
    6. Witter MP

    (2013) Superficially projecting principal neurons in layer V of medial entorhinal cortex in the rat receive excitatory retrosplenial input

    The Journal of Neuroscience 33:15779–15792.

    https://doi.org/10.1523/JNEUROSCI.2646-13.2013

    • PubMed
    • Google Scholar
22. 1. Dalton MA
    2. Maguire EA

    (2017) The pre/parasubiculum: a hippocampal hub for scene-based cognition?

    Current Opinion in Behavioral Sciences 17:34–40.

    https://doi.org/10.1016/j.cobeha.2017.06.001

    • PubMed
    • Google Scholar
23. 1. Dalton MA
    2. Zeidman P
    3. McCormick C
    4. Maguire EA

    (2018) Differentiable processing of objects, associations, and scenes within the hippocampus

    The Journal of Neuroscience 38:8146–8159.

    https://doi.org/10.1523/JNEUROSCI.0263-18.2018

    • PubMed
    • Google Scholar
24. 1. Dalton MA
    2. McCormick C
    3. Maguire EA

    (2019) Differences in functional connectivity along the anterior-posterior axis of human hippocampal subfields

    NeuroImage 192:38–51.

    https://doi.org/10.1016/j.neuroimage.2019.02.066

    • PubMed
    • Google Scholar
25. 1. de Flores R
    2. Berron D
    3. Ding SL
    4. Ittyerah R
    5. Pluta JB
    6. Xie L
    7. Adler DH
    8. Robinson JL
    9. Schuck T
    10. Trojanowski JQ
    11. Grossman M
    12. Liu W
    13. Pickup S
    14. Das SR
    15. Wolk DA
    16. Yushkevich PA
    17. Wisse LEM

    (2020) Characterization of hippocampal subfields using ex vivo MRI and histology data: lessons for in vivo segmentation

    Hippocampus 30:545–564.

    https://doi.org/10.1002/hipo.23172

    • PubMed
    • Google Scholar
26. 1. DeKraker J
    2. Ferko KM
    3. Lau JC
    4. Köhler S
    5. Khan AR

    (2018) Unfolding the hippocampus: an intrinsic coordinate system for subfield segmentations and quantitative mapping

    NeuroImage 167:408–418.

    https://doi.org/10.1016/j.neuroimage.2017.11.054

    • PubMed
    • Google Scholar
27. Book

    1. Deshmukh SS

    (2014) Spatial and nonspatial representations in the lateral entorhinal cortex

    In: Derdikman D, Knierim JJ, editors. Space,Time and Memory in the Hippocampal Formation. Springer Vienna. pp. 127–152.

    https://doi.org/10.1007/978-3-7091-1292-2_6

    • PubMed
    • Google Scholar
28. 1. Desikan RS
    2. Ségonne F
    3. Fischl B
    4. Quinn BT
    5. Dickerson BC
    6. Blacker D
    7. Buckner RL
    8. Dale AM
    9. Maguire RP
    10. Hyman BT
    11. Albert MS
    12. Killiany RJ

    (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

    NeuroImage 31:968–980.

    https://doi.org/10.1016/j.neuroimage.2006.01.021

    • PubMed
    • Google Scholar
29. 1. Ding SL
    2. Van Hoesen GW

    (2015) Organization and detailed parcellation of human hippocampal head and body regions based on a combined analysis of cyto- and chemoarchitecture

    The Journal of Comparative Neurology 523:2233–2253.

    https://doi.org/10.1002/cne.23786

    • PubMed
    • Google Scholar
30. 1. Doan TP
    2. Lagartos-Donate MJ
    3. Nilssen ES
    4. Ohara S
    5. Witter MP

    (2019) Convergent projections from perirhinal and postrhinal cortices suggest a multisensory nature of lateral, but not medial, entorhinal cortex

    Cell Reports 29:617–627.

    https://doi.org/10.1016/j.celrep.2019.09.005

    • PubMed
    • Google Scholar
31. 1. Eichenbaum H
    2. Yonelinas AP
    3. Ranganath C

    (2007) The medial temporal lobe and recognition memory

    Annual Review of Neuroscience 30:123–152.

    https://doi.org/10.1146/annurev.neuro.30.051606.094328

    • PubMed
    • Google Scholar
32. 1. Fischl B

    (2012) FreeSurfer

    NeuroImage 62:774–781.

    https://doi.org/10.1016/j.neuroimage.2012.01.021

    • PubMed
    • Google Scholar
33. 1. Franzmeier N
    2. Neitzel J
    3. Rubinski A
    4. Smith R
    5. Strandberg O
    6. Ossenkoppele R
    7. Hansson O
    8. Ewers M
    9. Alzheimer’s Disease Neuroimaging Initiative

    (2020) Functional brain architecture is associated with the rate of tau accumulation in alzheimer’s disease

    Nature Communications 11:347.

    https://doi.org/10.1038/s41467-019-14159-1

    • PubMed
    • Google Scholar
34. 1. Gavrilescu M
    2. Stuart GW
    3. Rossell S
    4. Henshall K
    5. McKay C
    6. Sergejew AA
    7. Copolov D
    8. Egan GF

    (2008) Functional connectivity estimation in fmri data: influence of preprocessing and time course selection

    Human Brain Mapping 29:1040–1052.

    https://doi.org/10.1002/hbm.20446

    • PubMed
    • Google Scholar
35. 1. Genon S
    2. Bernhardt BC
    3. La Joie R
    4. Amunts K
    5. Eickhoff SB

    (2021) The many dimensions of human hippocampal organization and (dys) function

    Trends in Neurosciences 44:977–989.

    https://doi.org/10.1016/j.tins.2021.10.003

    • PubMed
    • Google Scholar
36. 1. Grothe MJ
    2. Barthel H
    3. Sepulcre J
    4. Dyrba M
    5. Sabri O
    6. Teipel SJ
    7. Alzheimer’s Disease Neuroimaging Initiative

    (2017) In vivo staging of regional amyloid deposition

    Neurology 89:2031–2038.

    https://doi.org/10.1212/WNL.0000000000004643

    • PubMed
    • Google Scholar
37. 1. Haxby JV
    2. Grady CL
    3. Horwitz B
    4. Ungerleider LG
    5. Mishkin M
    6. Carson RE
    7. Herscovitch P
    8. Schapiro MB
    9. Rapoport SI

    (1991) Dissociation of object and spatial visual processing pathways in human extrastriate cortex

    PNAS 88:1621–1625.

    https://doi.org/10.1073/pnas.88.5.1621

    • PubMed
    • Google Scholar
38. 1. Henriksen EJ
    2. Colgin LL
    3. Barnes CA
    4. Witter MP
    5. Moser MB
    6. Moser EI

    (2010) Spatial representation along the proximodistal axis of CA1

    Neuron 68:127–137.

    https://doi.org/10.1016/j.neuron.2010.08.042

    • PubMed
    • Google Scholar
39. 1. Hodgetts CJ
    2. Voets NL
    3. Thomas AG
    4. Clare S
    5. Lawrence AD
    6. Graham KS

    (2017) Ultra-high-field fmri reveals a role for the subiculum in scene perceptual discrimination

    The Journal of Neuroscience 37:3150–3159.

    https://doi.org/10.1523/JNEUROSCI.3225-16.2017

    • PubMed
    • Google Scholar
40. 1. Igarashi KM
    2. Ito HT
    3. Moser EI
    4. Moser MBB

    (2014) Functional diversity along the transverse axis of hippocampal area CA1

    FEBS Letters 588:2470–2476.

    https://doi.org/10.1016/j.febslet.2014.06.004

    • PubMed
    • Google Scholar
41. 1. Jenkinson M
    2. Smith S

    (2001) A global optimisation method for robust affine registration of brain images

    Medical Image Analysis 5:143–156.

    https://doi.org/10.1016/s1361-8415(01)00036-6

    • PubMed
    • Google Scholar
42. 1. Kaufman SK
    2. Del Tredici K
    3. Thomas TL
    4. Braak H
    5. Diamond MI

    (2018) Tau seeding activity begins in the transentorhinal/entorhinal regions and anticipates phospho-tau pathology in alzheimer’s disease and part

    Acta Neuropathologica 136:57–67.

    https://doi.org/10.1007/s00401-018-1855-6

    • PubMed
    • Google Scholar
43. 1. Keene CS
    2. Bladon J
    3. McKenzie S
    4. Liu CD
    5. O’Keefe J
    6. Eichenbaum H

    (2016) Complementary functional organization of neuronal activity patterns in the perirhinal, lateral entorhinal, and medial entorhinal cortices

    The Journal of Neuroscience 36:3660–3675.

    https://doi.org/10.1523/JNEUROSCI.4368-15.2016

    • PubMed
    • Google Scholar
44. 1. Kharabian Masouleh S
    2. Plachti A
    3. Hoffstaedter F
    4. Eickhoff S
    5. Genon S

    (2020) Characterizing the gradients of structural covariance in the human hippocampus

    NeuroImage 218:116972.

    https://doi.org/10.1016/j.neuroimage.2020.116972

    • PubMed
    • Google Scholar
45. 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 of London. Series B, Biological Sciences 369:20130369.

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

    • PubMed
    • Google Scholar
46. 1. Koster R
    2. Chadwick MJ
    3. Chen Y
    4. Berron D
    5. Banino A
    6. Düzel E
    7. Hassabis D
    8. Kumaran D

    (2018) Big-loop recurrence within the hippocampal system supports integration of information across episodes

    Neuron 99:1342–1354.

    https://doi.org/10.1016/j.neuron.2018.08.009

    • PubMed
    • Google Scholar
47. 1. Kragel PA
    2. Koban L
    3. Barrett LF
    4. Wager TD

    (2018) Representation, pattern information, and brain signatures: from neurons to neuroimaging

    Neuron 99:257–273.

    https://doi.org/10.1016/j.neuron.2018.06.009

    • PubMed
    • Google Scholar
48. 1. Kriegeskorte N
    2. Mur M
    3. Bandettini PA

    (2008) Representational similarity analysis-connecting the branches of systems neuroscience

    Frontiers in Systems Neuroscience 2:4.

    https://doi.org/10.3389/neuro.06.004.2008

    • PubMed
    • Google Scholar
49. 1. Ku SP
    2. Nakamura NH
    3. Maingret N
    4. Mahnke L
    5. Yoshida M
    6. Sauvage MM

    (2017) Regional specific evidence for memory-load dependent activity in the dorsal subiculum and the lateral entorhinal cortex

    Frontiers in Systems Neuroscience 11:51.

    https://doi.org/10.3389/fnsys.2017.00051

    • PubMed
    • Google Scholar
50. 1. Lace G
    2. Savva GM
    3. Forster G
    4. de Silva R
    5. Brayne C
    6. Matthews FE
    7. Barclay JJ
    8. Dakin L
    9. Ince PG
    10. Wharton SB
    11. MRC-CFAS

    (2009) Hippocampal tau pathology is related to neuroanatomical connections: an ageing population-based study

    Brain 132:1324–1334.

    https://doi.org/10.1093/brain/awp059

    • PubMed
    • Google Scholar
51. 1. Lee H
    2. GoodSmith D
    3. Knierim JJ

    (2020) Parallel processing streams in the hippocampus

    Current Opinion in Neurobiology 64:127–134.

    https://doi.org/10.1016/j.conb.2020.03.004

    • PubMed
    • Google Scholar
52. 1. Libby LA
    2. Ekstrom AD
    3. Ragland JD
    4. Ranganath C

    (2012) Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging

    The Journal of Neuroscience 32:6550–6560.

    https://doi.org/10.1523/JNEUROSCI.3711-11.2012

    • PubMed
    • Google Scholar
53. 1. Maass A
    2. Berron D
    3. Libby LA
    4. Ranganath C
    5. Düzel E

    (2015) Functional subregions of the human entorhinal cortex

    eLife 4:e06426.

    https://doi.org/10.7554/eLife.06426

    • PubMed
    • Google Scholar
54. 1. Maass A
    2. Berron D
    3. Harrison TM
    4. Adams JN
    5. La Joie R
    6. Baker S
    7. Mellinger T
    8. Bell RK
    9. Swinnerton K
    10. Inglis B
    11. Rabinovici GD
    12. Düzel E
    13. Jagust WJ

    (2019) Alzheimer’s pathology targets distinct memory networks in the ageing brain

    Brain 142:2492–2509.

    https://doi.org/10.1093/brain/awz154

    • PubMed
    • Google Scholar
55. 1. Nakamura NH
    2. Flasbeck V
    3. Maingret N
    4. Kitsukawa T
    5. Sauvage MM

    (2013) Proximodistal segregation of nonspatial information in CA3: preferential recruitment of a proximal CA3-distal CA1 network in nonspatial recognition memory

    The Journal of Neuroscience 33:11506–11514.

    https://doi.org/10.1523/JNEUROSCI.4480-12.2013

    • PubMed
    • Google Scholar
56. 1. Nakazawa Y
    2. Pevzner A
    3. Tanaka KZ
    4. Wiltgen BJ

    (2016) Memory retrieval along the proximodistal axis of CA1

    Hippocampus 26:1140–1148.

    https://doi.org/10.1002/hipo.22596

    • PubMed
    • Google Scholar
57. 1. Navarro Schröder T
    2. Haak KV
    3. Zaragoza Jimenez NI
    4. Beckmann CF
    5. Doeller CF

    (2015) Functional topography of the human entorhinal cortex

    eLife 4:e06738.

    https://doi.org/10.7554/eLife.06738

    • PubMed
    • Google Scholar
58. 1. Nelson PT
    2. Alafuzoff I
    3. Bigio EH
    4. Bouras C
    5. Braak H
    6. Cairns NJ
    7. Castellani RJ
    8. Crain BJ
    9. Davies P
    10. Del Tredici K
    11. Duyckaerts C
    12. Frosch MP
    13. Haroutunian V
    14. Hof PR
    15. Hulette CM
    16. Hyman BT
    17. Iwatsubo T
    18. Jellinger KA
    19. Jicha GA
    20. Kövari E
    21. Kukull WA
    22. Leverenz JB
    23. Love S
    24. Mackenzie IR
    25. Mann DM
    26. Masliah E
    27. McKee AC
    28. Montine TJ
    29. Morris JC
    30. Schneider JA
    31. Sonnen JA
    32. Thal DR
    33. Trojanowski JQ
    34. Troncoso JC
    35. Wisniewski T
    36. Woltjer RL
    37. Beach TG

    (2012) Correlation of alzheimer disease neuropathologic changes with cognitive status: a review of the literature

    Journal of Neuropathology and Experimental Neurology 71:362–381.

    https://doi.org/10.1097/NEN.0b013e31825018f7

    • PubMed
    • Google Scholar
59. 1. Neunuebel JP
    2. Yoganarasimha D
    3. Rao G
    4. Knierim JJ

    (2013) Conflicts between local and global spatial frameworks dissociate neural representations of the lateral and medial entorhinal cortex

    The Journal of Neuroscience 33:9246–9258.

    https://doi.org/10.1523/JNEUROSCI.0946-13.2013

    • PubMed
    • Google Scholar
60. 1. Nilssen ES
    2. Doan TP
    3. Nigro MJ
    4. Ohara S
    5. Witter MP

    (2019) Neurons and networks in the entorhinal cortex: a reappraisal of the lateral and medial entorhinal subdivisions mediating parallel cortical pathways

    Hippocampus 29:1238–1254.

    https://doi.org/10.1002/hipo.23145

    • PubMed
    • Google Scholar
61. 1. Oltmer J
    2. Slepneva N
    3. Llamas Rodriguez J
    4. Greve DN
    5. Williams EM
    6. Wang R
    7. Champion SN
    8. Lang-Orsini M
    9. Nestor K
    10. Fernandez-Ros N
    11. Fischl B
    12. Frosch MP
    13. Magnain C
    14. van der Kouwe AJW
    15. Augustinack JC

    (2022) Quantitative and histologically validated measures of the entorhinal subfields in ex vivo MRI

    Brain Communications 4:fcac074.

    https://doi.org/10.1093/braincomms/fcac074

    • PubMed
    • Google Scholar
62. 1. O’Mara S

    (2006) Controlling hippocampal output: the central role of subiculum in hippocampal information processing

    Behavioural Brain Research 174:304–312.

    https://doi.org/10.1016/j.bbr.2006.08.018

    • PubMed
    • Google Scholar
63. 1. Palmqvist S
    2. Schöll M
    3. Strandberg O
    4. Mattsson N
    5. Stomrud E
    6. Zetterberg H
    7. Blennow K
    8. Landau S
    9. Jagust W
    10. Hansson O

    (2017) Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity

    Nature Communications 8:1–13.

    https://doi.org/10.1038/s41467-017-01150-x

    • PubMed
    • Google Scholar
64. 1. Paquola C
    2. Benkarim O
    3. DeKraker J
    4. Larivière S
    5. Frässle S
    6. Royer J
    7. Tavakol S
    8. Valk S
    9. Bernasconi A
    10. Bernasconi N
    11. Khan A
    12. Evans AC
    13. Razi A
    14. Smallwood J
    15. Bernhardt BC

    (2020) Convergence of cortical types and functional motifs in the human mesiotemporal lobe

    eLife 9:e60673.

    https://doi.org/10.7554/eLife.60673

    • PubMed
    • Google Scholar
65. Book

    1. Penny WD
    2. Friston KJ
    3. Ashburner JT
    4. Kiebel SJ
    5. Nichols TE

    (2011)

    Statistical Parametric Mapping: The Analysis of Functional Brain Images

    Elsevier.

    • Google Scholar
66. 1. Plachti A
    2. Eickhoff SB
    3. Hoffstaedter F
    4. Patil KR
    5. Laird AR
    6. Fox PT
    7. Amunts K
    8. Genon S

    (2019) Multimodal parcellations and extensive behavioral profiling tackling the hippocampus gradient

    Cerebral Cortex 29:4595–4612.

    https://doi.org/10.1093/cercor/bhy336

    • PubMed
    • Google Scholar
67. 1. Ranganath C
    2. Ritchey M

    (2012) Two cortical systems for memory-guided behaviour

    Nature Reviews. Neuroscience 13:713–726.

    https://doi.org/10.1038/nrn3338

    • PubMed
    • Google Scholar
68. 1. Reagh ZM
    2. Yassa MA

    (2014) Object and spatial mnemonic interference differentially engage lateral and medial entorhinal cortex in humans

    PNAS 111:E4264–E4273.

    https://doi.org/10.1073/pnas.1411250111

    • PubMed
    • Google Scholar
69. 1. Ritchey M
    2. Libby LA
    3. Ranganath C

    (2015) Cortico-hippocampal systems involved in memory and cognition: the PMAT framework

    Progress in Brain Research 219:45–64.

    https://doi.org/10.1016/bs.pbr.2015.04.001

    • PubMed
    • Google Scholar
70. 1. Robin J
    2. Moscovitch M

    (2017) Details, GIST and schema: hippocampal–neocortical interactions underlying recent and remote episodic and spatial memory

    Current Opinion in Behavioral Sciences 17:114–123.

    https://doi.org/10.1016/j.cobeha.2017.07.016

    • Google Scholar
71. 1. Robin J

    (2018) Spatial scaffold effects in event memory and imagination

    Wiley Interdisciplinary Reviews. Cognitive Science 9:e1462.

    https://doi.org/10.1002/wcs.1462

    • PubMed
    • Google Scholar
72. 1. Robin J
    2. Olsen RK

    (2019) Scenes facilitate associative memory and integration

    Learning & Memory 26:252–261.

    https://doi.org/10.1101/lm.049486.119

    • PubMed
    • Google Scholar
73. 1. Roussarie JP
    2. Yao V
    3. Rodriguez-Rodriguez P
    4. Oughtred R
    5. Rust J
    6. Plautz Z
    7. Kasturia S
    8. Albornoz C
    9. Wang W
    10. Schmidt EF
    11. Dannenfelser R
    12. Tadych A
    13. Brichta L
    14. Barnea-Cramer A
    15. Heintz N
    16. Hof PR
    17. Heiman M
    18. Dolinski K
    19. Flajolet M
    20. Troyanskaya OG
    21. Greengard P

    (2020) Selective neuronal vulnerability in alzheimer’s disease: a network-based analysis

    Neuron 107:821–835.

    https://doi.org/10.1016/j.neuron.2020.06.010

    • PubMed
    • Google Scholar
74. 1. Roy DS
    2. Kitamura T
    3. Okuyama T
    4. Ogawa SK
    5. Sun C
    6. Obata Y
    7. Yoshiki A
    8. Tonegawa S

    (2017) Distinct neural circuits for the formation and retrieval of episodic memories

    Cell 170:1000–1012.

    https://doi.org/10.1016/j.cell.2017.07.013

    • PubMed
    • Google Scholar
75. 1. Schultz H
    2. Sommer T
    3. Peters J

    (2015) The role of the human entorhinal cortex in a representational account of memory

    Frontiers in Human Neuroscience 9:628.

    https://doi.org/10.3389/fnhum.2015.00628

    • PubMed
    • Google Scholar
76. 1. Sugar J
    2. Witter MP
    3. van Strien NM
    4. Cappaert NLM

    (2011) The retrosplenial cortex: intrinsic connectivity and connections with the (para) hippocampal region in the rat: an interactive connectome

    Frontiers in Neuroinformatics 5:7.

    https://doi.org/10.3389/fninf.2011.00007

    • PubMed
    • Google Scholar
77. 1. Suzuki WA
    2. Naya Y

    (2014) The perirhinal cortex

    Annual Review of Neuroscience 37:39–53.

    https://doi.org/10.1146/annurev-neuro-071013-014207

    • PubMed
    • Google Scholar
78. 1. Syversen IF
    2. Witter MP
    3. Kobro-Flatmoen A
    4. Goa PE
    5. Navarro Schröder T
    6. Doeller CF

    (2021) Structural connectivity-based segmentation of the human entorhinal cortex

    NeuroImage 245:118723.

    https://doi.org/10.1016/j.neuroimage.2021.118723

    • PubMed
    • Google Scholar
79. 1. Ungerleider LG
    2. Haxby JV

    (1994) What’ and ‘where’ in the human brain

    Current Opinion in Neurobiology 4:157–165.

    https://doi.org/10.1016/0959-4388(94)90066-3

    • PubMed
    • Google Scholar
80. 1. van Strien NM
    2. Cappaert NLM
    3. Witter MP

    (2009) The anatomy of memory: an interactive overview of the parahippocampal-hippocampal network

    Nature Reviews. Neuroscience 10:272–282.

    https://doi.org/10.1038/nrn2614

    • PubMed
    • Google Scholar
81. 1. Vandrey B
    2. Duncan S
    3. Ainge JA

    (2021) Object and object-memory representations across the proximodistal axis of CA1

    Hippocampus 31:881–896.

    https://doi.org/10.1002/hipo.23331

    • PubMed
    • Google Scholar
82. 1. Vann SD
    2. Aggleton JP
    3. Maguire EA

    (2009) What does the retrosplenial cortex do?

    Nature Reviews. Neuroscience 10:792–802.

    https://doi.org/10.1038/nrn2733

    • PubMed
    • Google Scholar
83. 1. Vogel JW
    2. Iturria-Medina Y
    3. Strandberg OT
    4. Smith R
    5. Levitis E
    6. Evans AC
    7. Hansson O
    8. Alzheimer’s Disease Neuroimaging Initiative
    9. Swedish BioFinder Study

    (2020) Spread of pathological tau proteins through communicating neurons in human alzheimer’s disease

    Nature Communications 11:2612.

    https://doi.org/10.1038/s41467-020-15701-2

    • PubMed
    • Google Scholar
84. 1. Vos de Wael R
    2. Larivière S
    3. Caldairou B
    4. Hong SJ
    5. Margulies DS
    6. Jefferies E
    7. Bernasconi A
    8. Smallwood J
    9. Bernasconi N
    10. Bernhardt BC

    (2018) Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding

    PNAS 115:10154–10159.

    https://doi.org/10.1073/pnas.1803667115

    • PubMed
    • Google Scholar
85. 1. Whitfield-Gabrieli S
    2. Nieto-Castanon A

    (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks

    Brain Connectivity 2:125–141.

    https://doi.org/10.1089/brain.2012.0073

    • PubMed
    • Google Scholar
86. 1. Wilson DIG
    2. Watanabe S
    3. Milner H
    4. Ainge JA

    (2013) Lateral entorhinal cortex is necessary for associative but not nonassociative recognition memory

    Hippocampus 23:1280–1290.

    https://doi.org/10.1002/hipo.22165

    • PubMed
    • Google Scholar
87. 1. Witter MP
    2. Amaral DG

    (1991) Entorhinal cortex of the monkey: V-projections to the dentate gyrus, hippocampus, and subicular complex

    The Journal of Comparative Neurology 307:437–459.

    https://doi.org/10.1002/cne.903070308

    • PubMed
    • Google Scholar
88. 1. Witter MP
    2. Naber PA
    3. van Haeften T
    4. Machielsen WC
    5. Rombouts SA
    6. Barkhof F
    7. Scheltens P
    8. Lopes da Silva FH

    (2000) Cortico-hippocampal communication by way of parallel parahippocampal-subicular pathways

    Hippocampus 10:398–410.

    https://doi.org/10.1002/1098-1063(2000)10:4<398::AID-HIPO6>3.0.CO;2-K

    • PubMed
    • Google Scholar
89. 1. Witter MP
    2. Doan TP
    3. Jacobsen B
    4. Nilssen ES
    5. Ohara S

    (2017) Architecture of the entorhinal cortex a review of entorhinal anatomy in rodents with some comparative notes

    Frontiers in Systems Neuroscience 11:46.

    https://doi.org/10.3389/fnsys.2017.00046

    • PubMed
    • Google Scholar
90. 1. Witter MP
    2. Amaral DG

    (2020) The entorhinal cortex of the monkey: VI. organization of projections from the hippocampus, subiculum, presubiculum, and parasubiculum

    The Journal of Comparative Neurology 529:828–852.

    https://doi.org/10.1002/cne.24983

    • PubMed
    • Google Scholar
91. 1. Yeung LK
    2. Olsen RK
    3. Hong B
    4. Mihajlovic V
    5. D’Angelo MC
    6. Kacollja A
    7. Ryan JD
    8. Barense MD

    (2019) Object-in-place memory predicted by anterolateral entorhinal cortex and parahippocampal cortex volume in older adults

    Journal of Cognitive Neuroscience 31:711–729.

    https://doi.org/10.1162/jocn_a_01385

    • PubMed
    • Google Scholar
92. 1. Yushkevich PA
    2. Piven J
    3. Hazlett HC
    4. Smith RG
    5. Ho S
    6. Gee JC
    7. Gerig G

    (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability

    NeuroImage 31:1116–1128.

    https://doi.org/10.1016/j.neuroimage.2006.01.015

    • PubMed
    • Google Scholar
93. 1. Zaitsev M
    2. Hennig J
    3. Speck O

    (2004) Point spread function mapping with parallel imaging techniques and high acceleration factors: fast, robust, and flexible method for echo-planar imaging distortion correction

    Magnetic Resonance in Medicine 52:1156–1166.

    https://doi.org/10.1002/mrm.20261

    • PubMed
    • Google Scholar
94. 1. Zeidman P
    2. Lutti A
    3. Maguire EA

    (2015) Investigating the functions of subregions within anterior hippocampus

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 73:240–256.

    https://doi.org/10.1016/j.cortex.2015.09.002

    • PubMed
    • Google Scholar
95. 1. Ziontz J
    2. Adams JN
    3. Harrison TM
    4. Baker SL
    5. Jagust WJ

    (2021) Hippocampal connectivity with retrosplenial cortex is linked to neocortical tau accumulation and memory function

    The Journal of Neuroscience 41:8839–8847.

    https://doi.org/10.1523/JNEUROSCI.0990-21.2021

    • PubMed
    • Google Scholar

Author details

1. Xenia Grande

   1. Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University Magdeburg, Magdeburg, Germany
   2. German Center for Neurodegenerative Diseases, Magdeburg, Germany

   Contribution

   Conceptualization, Formal analysis, Visualization, Writing - original draft, Writing – review and editing, Data Interpretation

   For correspondence

   xenia.grande@dzne.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2486-3201

2. Magdalena M Sauvage

   1. Functional Architecture of Memory Department, Leibniz-Institute for Neurobiology, Magdeburg, Germany
   2. Functional Neuroplasticity Department, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
   3. Center for Behavioral Brain Sciences, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

   Contribution

   Funding acquisition, Writing – review and editing, Data interpretation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7586-6410

3. Andreas Becke

   1. Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University Magdeburg, Magdeburg, Germany
   2. German Center for Neurodegenerative Diseases, Magdeburg, Germany

   Contribution

   Investigation, Project administration

   Competing interests

   No competing interests declared

4. Emrah Düzel

   1. Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University Magdeburg, Magdeburg, Germany
   2. German Center for Neurodegenerative Diseases, Magdeburg, Germany
   3. Institute of Cognitive Neuroscience, University College London, London, United Kingdom

   Contribution

   Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

5. David Berron

   1. German Center for Neurodegenerative Diseases, Magdeburg, Germany
   2. Center for Behavioral Brain Sciences, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
   3. Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing, Data Interpretation

   For correspondence

   david.berron@dzne.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1558-1883

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