In the early 1950s, an American named Henry Molaison underwent an experimental type of brain surgery to treat his severe epilepsy. The surgeon removed a region of the brain known as the temporal lobe from both sides of his brain. After the surgery, Molaison's epilepsy was greatly improved, but he was also left with a profound amnesia, unable to form new memories of recent events.

Subsequent experiments, including many with Molaison himself as a subject, have attempted to identify the roles of the various structures within the temporal lobes. The hippocampus—which is involved in memory and spatial navigation—has received the most attention, but in recent years a region called the entorhinal cortex has also come to the fore. Known as the gateway to the hippocampus, the entorhinal cortex relays sensory information from the outer cortex of the brain to the hippocampus.

In rats and mice the entorhinal cortex can be divided into two subregions that have distinct connections to other parts of the temporal lobe and to the rest of the brain. These are the medial entorhinal cortex, which is the subregion nearest the centre of the brain, and the lateral entorhinal cortex, which is to the left or right of the centre.

For many years researchers had assumed that human entorhinal subregions were located simply to the center or to the sides of the brain. However, it was difficult to check this as the entorhinal cortex measures less than 1 cm across, which placed it beyond the reach of most brain-imaging techniques. Now, two independent groups of researchers have used a technique called functional magnetic resonance imaging to show a different picture. The fMRI data—which were collected in a magnetic field of 7 Tesla, rather than the 1.5 Tesla used in previous experiments—reveal that the entorhinal cortex is predominantly divided from front-to-back in humans.

One of the groups—Navarro Schröder, Haak et al.—used three different sets of functional MRI data to show that the human entorhinal cortex has anterior-lateral and posterior-medial subregions. In one of these experiments, functional MRI was used to measure activity across the whole brain as subjects performed a virtual reality task: this task included some components that involved spatial navigation and other components that did not. The other group—Maass, Berron et al.—used the imaging data to show that the pattern of connections between the anterior-lateral subregion and the hippocampus was different to that between the posterior-medial subregion and the hippocampus.

The discovery of these networks in the temporal lobe in humans will help to bridge the gap between studies of memory in rodents and in humans. Given that the lateral entorhinal cortex is one of the first regions to be affected in Alzheimer's disease, identifying the specific properties and roles of these networks could also provide insights into disease mechanisms.

The entorhinal cortex (EC)—defining the interface between the hippocampus and the neocortex (Munoz and Insausti, 2005)—plays a pivotal role in the integration of different sensory inputs into higher order mnemonic representations (Eichenbaum et al., 2007; Moser and Moser, 2013). In rodents—and on the basis of cytoarchitectonics—the EC is typically (Kerr et al., 2007; Canto et al., 2008; Van Strien et al., 2009) subdivided into two major subregions, the medial- and the lateral entorhinal cortex (MEC and LEC, respectively). The MEC receives inputs about spatial information from parahippocampal cortex (PHC) and the LEC receives item-related information from perirhinal cortex (PRC) (Van Strien et al., 2009; Deshmukh and Knierim, 2011; Ranganath and Ritchey, 2012; Knierim et al., 2013). Similar functional roles of the PHC and PRC have been described in humans (Epstein and Kanwisher, 1998; Davachi et al., 2003; Eichenbaum et al., 2007; Ekstrom and Bookheimer, 2007; Litman et al., 2009; Duarte et al., 2011; Staresina et al., 2011; Martin et al., 2013; Vilberg and Davachi, 2013) and relate to distinct visual processing streams (Kravitz et al., 2011). The differential input pattern into the rodent LEC and MEC also dovetails with a cell-type specific functional specialisation (Eichenbaum and Lipton, 2008). The MEC contains a high proportion of head-direction and grid cells, whose activity is modulated by running direction and spatial location, respectively (Hafting et al., 2005; Sargolini et al., 2006). In contrast, cells in the LEC respond to individual objects in the environment rather than to specific locations (Deshmukh and Knierim, 2011; Tsao et al., 2013; Knierim et al., 2013).

Despite a wealth of data and marked differences in structure and function of the rodent MEC and LEC evidence for their human homologue remains elusive. This hampers translational studies, which is particularly relevant in the case of Alzheimer's disease (AD) with AD pathology starting in the EC (Braak and Braak, 1992). Within the EC, the vulnerability to AD-related pathology is not homogeneously distributed and differs between medial and lateral strips in humans, which has been related to similar findings in the rodent MEC and LEC, respectively (Khan et al., 2014). However, the localization of the human homologue of the rodent MEC and LEC remains unclear. A source of considerable confusion is the fact that ‘MEC’ and ‘LEC’ are referring to cytoarchitectonically defined areas and not to anatomical locations. Hence, they do not circumscribe strips of medial and lateral EC. Rather, the MEC is located medially in the septal (posterior) part of the EC and the LEC is located laterally in the temporal (anterior) part of the EC in rodents (Van Strien et al., 2009). Furthermore, tracing studies on PHC and PRC pathways in non-human primates suggest a dominant anterior-posterior division (Suzuki and Amaral, 1994; Insausti and Amaral, 2008), as do single-unit recordings that show activity consistent with the rodent LEC in the anterior EC in primates (Killian et al., 2012). In contrast, neuroimaging studies on memory in healthy participants (Schultz et al., 2012; Reagh and Yassa, 2014) and participants with preclinical AD (Khan et al., 2014) suggest that the rodent MEC and LEC map on medial and lateral strips of EC in humans.

To resolve this discrepancy in the literature, one needs to investigate the relatively small EC (25–30 mm² in humans) (Krimer et al., 1997) with high anatomical precision. An earlier study investigated entorhinal connectivity with high-resolution functional magnetic resonance imaging (fMRI), but averaged signal changes over the entire region (Lacy and Stark, 2012). To achieve higher resolution imaging, here we leveraged high-field, sub-millimetre fMRI at 7 T and sought to identify the human homologue of the rodent MEC and LEC by informing our analysis by well-known functional and structural properties of the EC. Specifically, it has been shown that MEC and LEC exhibit differential connectivity with cortical regions (Witter and Groenewegen, 1989; Kerr et al., 2007; Van Strien et al., 2009). The differential fingerprints of anatomical connectivity should lead to differences in functional connectivity identifiable with fMRI (Johansen-Berg et al., 2004; Buckner et al., 2013; Wang et al., 2014). To test patterns of functional connectivity, we measured whole-brain activity while participants performed a virtual reality task with spatial and non-spatial components and validated the results in publicly available resting-state data from the WU-Minn Human Connectome Project (Van Essen et al., 2013; Smith et al., 2013) (HCP—www.humanconnectome.org). In addition, differential sensitivity to spatial and non-spatial stimuli could provide converging evidence to identify the human homologue of the rodent MEC and LEC, which we tested in a third, independent dataset. A complementary approach to the global network perspective presented here is given by Maass et al. (Maass et al., 2015) who scrutinized the fine-grained connectivity pattern of medial temporal lobe regions with the EC.

A recent model on cortical memory networks (Ranganath and Ritchey, 2012) posits that an anterior-temporal (AT) system converges on the PRC and a posterior-medial (PM) system on the PHC. Based on studies in rodents, the two networks are hypothesised to connect to either the LEC or the MEC, respectively (Witter and Groenewegen, 1989; Kerr et al., 2007; Van Strien et al., 2009). Studies in non-human primates predict that the entorhinal projections of the two systems show a strong anteroposterior division (Suzuki and Amaral, 1994). In order to test this prediction and to elucidate the role of the EC, we first applied a model-based approach on fMRI data acquired while participants were performing a virtual-reality navigation task to directly mimic studies in rodents (see ‘Materials and methods’ for details). This task targeted all entorhinal systems, because it involved both navigation-related spatial components and processing of non-spatial stimuli.

We created spherical regions-of-interest (ROIs) with 4 mm radius around coordinates pertaining to either of the networks (Libby et al., 2012; Ranganath and Ritchey, 2012) (see Table 1), as well as ROIs for both the medial and lateral half, and anterior and posterior half of the EC to ensure comparable number of voxels per parcel and therefore comparable signal-to-noise ratio (SNR) properties. Then we computed seed-based connectivity from the two neocortical networks to either sets of EC ROIs, see Figure 1. We found a main effect of network on entorhinal connectivity (repeated-measures ANOVA: F_(1,21) = 10.0, p = 0.005). Post-hoc t-tests revealed that the lateral parts of EC connected stronger to the AT compared to the PM network (T₍₂₁₎ = 2.6, p = 0.015; medial parts of EC: T₍₂₁₎ = −0.2 p = 0.83). However, in contrast to previous suggestions, we additionally observed a connectivity difference along the anteroposterior axis (repeated-measures ANOVA: main effect of network, F_(1,21) = 13.2, p = 0.001) and post-hoc t-tests showed that the anterior parts of EC connected more with the AT compared to the PM network (T₍₂₁₎ = 2.7: p = 0.01; posterior EC: T₍₂₁₎ = −0.49, p = 0.63).

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In a second step, we wanted to overcome potential limitations of the seed-based analysis. For example, the selected volume and location of neocortical seed regions could introduce biases (e.g., spatial proximity of the seeds) and imperfect normalisation procedures could affect the results particularly in the frontal lobes where projections from both the rodent LEC and MEC are neighbouring (Kerr et al., 2007). In addition, manual subdivision of the EC along cardinal axes likely misrepresents cytoarchitectonic boundaries. Therefore, we adopted a complementary approach to trace the dominant modes of functional connectivity change within the EC in a fully data-driven manner (Haak et al., 2014) (see ‘Materials and methods’). In brief, for every voxel in the EC, we determined its functional connectivity fingerprint with respect to the rest of cortex and used these fingerprints to compute the pair-wise similarities among all voxels within the ROI. The ensuing (voxels-by-voxels) similarity matrix was then fed to the Laplacian Eigenmaps (LE) algorithm (Belkin and Niyogi, 2003), which has previously also been successfully applied to trace changes in white-matter tractography (Johansen-Berg et al., 2004; Cerliani et al., 2012) and resting-state fMRI connectivity (Haak et al., 2014). The LE algorithm projects the high-dimensional, voxel-wise connectivity data onto a series of one-dimensional vectors, with the requirement that the similarities among the connectivity fingerprints are maximally preserved (in the vein of e.g., multidimensional scaling). These vectors represent multiple, spatially overlapping maps (as revealed by colour-coding the EC voxels according to the vectors' values) and are sorted according to how well they preserve the similarities among the original, high-dimensional connectivity fingerprints. Thus, the first vector represents the dominant mode of connectivity change in the EC, the second represents the second-dominant mode, and so on.

Applied to the fMRI data acquired while subjects performed the virtual-reality task, we observed that the dominant mode of functional connectivity change extended along the long-axis of the EC, approximately from the posterior to the anterior end (Figure 2 and Figure 2—figure supplement 1), while the orientation of the second was largely perpendicular (Figure 3 and Figure 3—figure supplement 1). Both modes of connectivity change could also be reliably detected using an independent resting-state fMRI dataset (60 subjects of the WU-Minn Human Connectome Project; see ‘Materials and methods’), suggesting that the organization of EC functional connectivity is largely task-independent (Figure 4). Both the first and second-dominant modes of functional connectivity change were highly reproducible across resting-state sessions (Pearson's R = 0.99, p < 0.001 and Pearson's R = 0.98, p < 0.001, for the dominant and second-dominant modes, respectively).

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Figure 4

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Furthermore, in order to identify the potential human homologues of the rodent LEC and MEC, we clustered the vectors representing the dominant and second-dominant modes of functional connectivity change (separately) through a median-split approach (see ‘Materials and methods’). Hence, each cluster comprises 50% of voxels in the EC. 3D-rendering of the two clusters derived from the dominant mode of connectivity change revealed a consistent topology across hemispheres (Figure 2B, Video 1). One division contained the posterior EC (Figure 2—figure supplement 1—displayed in blue). The other division included most of the anterior EC (Figure 2—figure supplement 1—displayed in red). In addition to the dominant anterior-posterior distinction, the posterior cluster was located more medially (and to some extent more dorsally) and the anterior cluster was located more laterally (and to some extent more ventrally). Hereafter, we refer to the clusters as posterior-medial EC (‘pmEC’) and anterior-lateral EC (‘alEC’), respectively, consistent with Maass et al. (Maass et al., 2015). Clusters derived from the second-dominant mode were less consistent across hemispheres, but showed an approximately orthogonal orientation relative to the first (Figure 3 and Figure 3—figure supplement 1).

Video 1

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If the two clusters (i.e., EC halves) derived from the dominant mode of functional connectivity change correspond to the homologues of the rodent LEC and MEC, their whole-brain connectivity profiles should correspond to the known connectivity profiles in rodents and resemble the AT and PM system proposed by Ranganath and Ritchey (Ranganath and Ritchey, 2012). To test this hypothesis, we computed whole-volume connectivity maps of the two clusters. Group-level contrasts showed peaks in the medial-prefrontal and orbitofrontal cortex for the alEC, regions associated with the AT system. In contrast, occipital and posterior-parietal cortex was dominated by connectivity with the pmEC, areas associated with the PM system (Ranganath and Ritchey, 2012). In addition, the pmEC showed increased connectivity with frontal regions (see Figure 2D). These findings are in line with the patterns of reciprocal connections of the rodent LEC and MEC, respectively (Kerr et al., 2007). Notably, this was not the case for the connectivity maps of the two clusters derived from the second-dominant mode of connectivity change (Figure 3D) that were widely dominated by only one of the clusters.

Furthermore, we examined both spatial and temporal SNR (tSNR) of the alEC and the pmEC (Figure 2—figure supplement 3). tSNR did not differ between alEC and pmEC (T₍₂₁₎ = 0.2, p = 0.83) but spatial SNR did (T₍₂₁₎ = 9.7, p < 0.001). This was associated with higher signal in the pmEC compared to the alEC (mean signal: alEC = 4.97; mean signal pmEC = 7.76; T₍₂₁₎ = 38, p < 0.001) in the absence of differences in spatial standard deviation (T₍₂₁₎ = 1.5, p = 0.144). Note, that mean signal was subtracted from time-series prior to all connectivity analyses (see ‘Materials and methods’), which makes it unlikely that signal intensity differences affected the connectivity results.

We used an independent component analysis (ICA)-based method for data cleaning (see ‘Materials and methods’) that has been shown to efficiently remove residual effects of head motion. However, we additionally repeated the data-driven connectivity analysis after excluding time periods with large head movements (motion scrubbing [Power et al., 2012, 2015]). Motion scrubbing had only minimal effects on the results. Pearson correlation coefficients of the pre- and post scrubbing results were close to 1 and highly significant (gradient one: left R = 0.9964, right R = 0.9958; gradient two: left = 0.9348, right = 0.8816; all p values <0.001).

We also tested if the alEC and pmEC exhibited stronger connectivity with their potential homologue region in the contralateral hemisphere compared to each other. Here we observed that homologous connectivity indeed exceeded non-homologous connectivity (Figure 2—figure supplement 4). Importantly, this was also the case if non-homologous connectivity was assessed within the same hemisphere, between adjacent parts of the EC (T₍₂₁₎ = 4.05, p = 0.0006).

Finally, studies on rodent electrophysiology (Deshmukh and Knierim, 2011; Knierim et al., 2013) predict that the LEC and its human homologue should respond preferentially to non-spatial stimuli, whereas the MEC and its human homologue should be involved in processing spatial information. We tested this prediction by conducting a second fMRI study at 7 T in which an independent group of participants was presented with spatial (pictures of scenes) and non-spatial stimuli (pictures of objects), see ‘Materials and methods’ for details. We contrasted fMRI responses to spatial and non-spatial stimuli. Here, we observed higher responses to spatial than non-spatial stimuli in the posterior EC, while the inverse contrast (objects vs scenes) showed higher responses in the anterior EC (Figure 2C). ROI analyses using the clusters derived from the dominant mode of connectivity change revealed that the anterior-lateral cluster showed higher sensitivity to non-spatial stimuli compared to the PM cluster (T₍₂₀₎ = 4.9, p = 0.0001, Figure 2C). This dissociation was not present for the clusters that were derived from the second-dominant mode of connectivity change within the EC (T₍₂₀₎ = −0.26, p = 0.8; Figure 3C).

In sum, our results suggest that the human homologue of the rodent MEC maps predominantly on the human posterior parts of the EC, while the homologue of the rodent LEC maps predominantly on the anterior parts of the EC.

The EC, in concert with the hippocampus, plays a crucial role in memory and learning (Eichenbaum et al., 2007) and is the core of the brain's navigational system (Moser and Moser, 2013). While the shape and location of the EC differs between rodents and primates (Witter and Groenewegen, 1989), the anatomical organisation and connectivity patterns are largely conserved across species (Canto et al., 2008). However, translational studies on EC subregions faced the problem of identifying homologous regions across species. For example, recent neuroimaging studies on mnemonic processing (Schultz et al., 2012; Reagh and Yassa, 2014) and Alzheimer's pathology (Khan et al., 2014) directly related medial and lateral strips of EC in humans to the rodent MEC and LEC. However, the anatomical locations of these cytoarchitectonically defined regions in rodents differ along more than only the mediolateral axis. More specifically, the LEC is situated more anterior-ventrally, whereas the MEC is situated more posterior-dorsally in rodents (Van Strien et al., 2009). Therefore, it is unlikely that medial and lateral strips of EC in humans correspond to the rodent MEC and LEC, respectively. Furthermore, in primates the characteristic projections from the PRC and PHC strongly map onto the anteroposterior axis (Suzuki and Amaral, 1994). Here, we leveraged the distinct connectivity fingerprints and functional roles (such as complementary computation of scene and object information) of the rodent LEC and MEC to find their human homologues with fMRI with three complementary methods and three independent datasets. Both model-based and data-driven connectivity analyses, as well as sensitivity to non-spatial vs spatial stimuli provide evidence for an anterior-lateral and a PM localisation of the homologues of the rodent LEC and MEC, respectively. Maass et al confirmed these findings in a study with two high-resolution, high-field fMRI datasets by focusing on local connectivity between regions of the medial temporal lobes. They found preferential connectivity of PRC and proximal subiculum to anterior-lateral parts of the EC, whereas posterior-medial parts of the EC were more connected to PHC and distal subiculum. This corresponds well with our findings (Figure 2—figure supplement 1). In line with the present study, Maass et al (Maass et al., 2015) report local connectivity fingerprints of the human anterior-lateral and posterior-medial EC that mimicked those of the rodent LEC and MEC, respectively.

In addition to the change in functional connectivity from PM to anterior-lateral, our data-driven connectivity analysis also revealed a second organisation structure approximately perpendicular to the first (Figure 3—figure supplement 1), which might reflect bands of intra-entorhinal projections that are known to cross the LEC/MEC boundary in a roughly orthogonal orientation in rodents (Canto et al., 2008) and in primates (Chrobak and Amaral, 2007).

The selective sensitivity to spatial and non-spatial information, or ‘context vs content’ more broadly (Knierim et al., 2013), points towards fundamental difference in computations of the LEC and MEC. How to characterise those differences most accurately remains an open question (Knierim et al., 2013), but our results can help to inform future studies on the role of the human alEC and pmEC in higher-level cognition.

Notably, the present findings confirm three out of four complementary criteria for the definition of cortical areas that have traditionally been advocated (Van Essen, 1985), namely topographic organization, connectivity and functional properties (the fourth one being cyto- and myeloarchitectonic organization).

Previous neuroimaging studies in humans reported differences between medial and lateral aspects of EC that mimicked differences between the rodent MEC and LEC and assumed that both subregions are present on coronal slices of the EC (Schultz et al., 2012; Khan et al., 2014; Reagh and Yassa, 2014), that is, that the MEC and LEC correspond to medial and lateral strips of the EC. In light of our findings, these reports could be explained by a partial overlap of the medial and lateral divisions with the pmEC and the alEC, respectively. For example, we noticed a mediolateral difference of responses to spatial and non-spatial stimuli on some coronal slices (Figure 2—figure supplement 2B). However, our results suggest that coronal slices through the most posterior EC exclusively harbour the human homologue of the rodent MEC. Similarly, anterior slices appear to contain mostly the homologue of the rodent LEC. Hence, improved mapping of homologous regions between rodents and humans should lead to increased effect sizes and more accurate interpretations.

In summary, the present findings can help to inform future translational research on the role of entorhinal subregions in fields ranging from clinical neuroscience, such as on the early progression of Alzheimer's disease, to cognitive neuroscience, for example, nature and mechanisms of different forms of memory and their integration into higher order representations (Eichenbaum and Lipton, 2008).

1. 1. Augustinack JC
   2. Magnain C
   3. Reuter M
   4. van der Kouwe AJ
   5. Boas D
   6. Fischl B

   (2013) MRI Parcellation of Ex Vivo medial temporal lobe

   Neuroimage 93:252–259.

   https://doi.org/10.1016/j.neuroimage.2013.05.053

   • 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

   • Google Scholar
3. 1. Belkin M
   2. Niyogi P

   (2003) Laplacian eigenmaps for dimensionality Reduction and data representation

   Neural Computation 15:1373–1396.

   https://doi.org/10.1162/089976603321780317

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

   (1992) The human entorhinal cortex: normal morphology and lamina-specific pathology in various diseases

   Neuroscience Research 15:6–31.

   https://doi.org/10.1016/0168-0102(92)90014-4

   • Google Scholar
5. 1. Buckner RL
   2. Krienen FM
   3. Yeo BTT

   (2013) Opportunities and limitations of intrinsic functional connectivity MRI

   Nature Neuroscience 16:832–837.

   https://doi.org/10.1038/nn.3423

   • 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 2008:381243.

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

   • Google Scholar
7. 1. Cerliani L
   2. Thomas RM
   3. Jbabdi S
   4. Siero JC
   5. Nanetti L
   6. Crippa A
   7. Gazzola V
   8. D'Arceuil H
   9. Keysers C

   (2012) Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex

   Human Brain Mapping 33:2005–2034.

   https://doi.org/10.1002/hbm.21338

   • Google Scholar
8. 1. Chrobak JJ
   2. Amaral DG

   (2007) Entorhinal cortex of the monkey: VII. Intrinsic connections

   The Journal of Comparative Neurology 500:612–633.

   https://doi.org/10.1002/cne.21200

   • Google Scholar
9. 1. Cohen AL
   2. Fair DA
   3. Dosenbach NU
   4. Miezin FM
   5. Dierker D
   6. Van Essen DC
   7. Schlaggar BL
   8. Petersen SE

   (2008) Defining functional areas in individual human brains using resting functional connectivity MRI

   Neuroimage 41:45–57.

   https://doi.org/10.1016/j.neuroimage.2008.01.066

   • Google Scholar
10. 1. Cusack R
    2. Vicente-Grabovetsky A
    3. Mitchell DJ
    4. Wild CJ
    5. Auer T
    6. Linke AC
    7. Peelle JE

    (2015) Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML

    Frontiers in Neuroinformatics 8:90.

    https://doi.org/10.3389/fninf.2014.00090

    • Google Scholar
11. 1. Davachi L
    2. Mitchell JP
    3. Wagner AD

    (2003) Multiple routes to memory: distinct medial temporal lobe processes build item and source memories

    Proceedings of the National Academy of Sciences of USA 100:2157–2162.

    https://doi.org/10.1073/pnas.0337195100

    • Google Scholar
12. 1. Deshmukh SS
    2. Knierim JJ

    (2011) Representation of non-spatial and spatial information in the lateral entorhinal cortex

    Frontiers in Behavioral Neuroscience 5:69.

    https://doi.org/10.3389/fnbeh.2011.00069

    • Google Scholar
13. 1. Doeller CF
    2. Barry C
    3. Burgess N

    (2010) Evidence for grid cells in a human memory network

    Nature 463:657–661.

    https://doi.org/10.1038/nature08704

    • Google Scholar
14. 1. Doeller CF
    2. King JA
    3. Burgess N

    (2008) Parallel striatal and hippocampal systems for landmarks and boundaries in spatial memory

    Proceedings of the National Academy of Sciences of USA 105:5915–5920.

    https://doi.org/10.1073/pnas.0801489105

    • Google Scholar
15. 1. Duarte A
    2. Henson RN
    3. Graham KS

    (2011) Stimulus content and the neural correlates of source memory

    Brain Research 1373:110–123.

    https://doi.org/10.1016/j.brainres.2010.11.086

    • Google Scholar
16. 1. Eichenbaum H
    2. Lipton PA

    (2008) Towards a functional organization of the medial temporal lobe memory system: role of the parahippocampal and medial entorhinal cortical areas

    Hippocampus 18:1314–1324.

    https://doi.org/10.1002/hipo.20500

    • Google Scholar
17. 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

    • Google Scholar
18. 1. Ekstrom A
    2. Bookheimer S

    (2007) Spatial and temporal episodic memory retrieval recruit dissociable functional networks in the human brain

    Learning & Memory 14:645–654.

    https://doi.org/10.1101/lm.575107

    • Google Scholar
19. 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

    • Google Scholar
20. 1. Frankó E
    2. Insausti AM
    3. Artacho-Pérula E
    4. Insausti R
    5. Chavoix C

    (2014) Identification of the human medial temporal lobe regions on magnetic resonance images

    Human Brain Mapping 35:248–256.

    https://doi.org/10.1002/hbm.22170

    • Google Scholar
21. 1. Haak KV
    2. Jbabdi S
    3. Beckmann CF

    (2014)

    ConGrads! A framework for mapping connectivity gradients with resting-state FMRI

    Poster presented at the 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, Germany.

    • Google Scholar
22. 1. Hafting T
    2. Fyhn M
    3. Molden S
    4. Moser M-B
    5. Moser EI

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

    Nature 436:801–806.

    https://doi.org/10.1038/nature03721

    • Google Scholar
23. 1. Insausti R
    2. Amaral DG

    (2008) Entorhinal cortex of the monkey: IV. Topographical and laminar organization of cortical afferents

    The Journal of Comparative Neurology 509:608–641.

    https://doi.org/10.1002/cne.21753

    • Google Scholar
24. 1. Insausti R
    2. Juottonen K
    3. Soininen H
    4. Insausti AM
    5. Partanen K
    6. Vainio P
    7. Laakso MP
    8. Pitkänen A

    (1998)

    MR volumetric analysis of the human entorhinal, perirhinal, and temporopolar cortices

    AJNR. American Journal of Neuroradiology 19:659–671.

    • Google Scholar
25. 1. Johansen-Berg H
    2. Behrens TE
    3. Robson MD
    4. Drobnjak I
    5. Rushworth MF
    6. Brady JM
    7. Smith SM
    8. Higham DJ
    9. Matthews PM

    (2004) Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex

    Proceedings of the National Academy of Sciences of USA 101:13335–13340.

    https://doi.org/10.1073/pnas.0403743101

    • Google Scholar
26. 1. Kerr KM
    2. Agster KL
    3. Furtak SC
    4. Burwell RD

    (2007) Functional Neuroanatomy of the parahippocampal region: the lateral and medial entorhinal areas

    Hippocampus 17:697–708.

    https://doi.org/10.1002/hipo.20315

    • Google Scholar
27. 1. Khan UA
    2. Liu L
    3. Provenzano FA
    4. Berman DE
    5. Profaci CP
    6. Sloan R
    7. Mayeux R
    8. Duff KE
    9. Small SA

    (2014) Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer's disease

    Nature Neuroscience 17:304–311.

    https://doi.org/10.1038/nn.3606

    • Google Scholar
28. 1. Killian NJ
    2. Jutras MJ
    3. Buffalo EA

    (2012) A map of visual space in the primate entorhinal cortex

    Nature 491:761–764.

    https://doi.org/10.1038/nature11587

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

    (2013) 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

    • Google Scholar
30. 1. Kravitz DJ
    2. Saleem KS
    3. Baker CI
    4. Mishkin M

    (2011) A new neural framework for visuospatial processing

    Nature Reviews Neuroscience 12:217–230.

    https://doi.org/10.1038/nrn3008

    • Google Scholar
31. 1. Krimer LS
    2. Hyde TM
    3. Herman MM
    4. Saunders RC

    (1997) The entorhinal cortex: an examination of cyto- and myeloarchitectonic organization in humans

    Cerebral Cortex 7:722–731.

    https://doi.org/10.1093/cercor/7.8.722

    • Google Scholar
32. 1. Lacy JW
    2. Stark CEL

    (2012) Intrinsic functional connectivity of the human medial temporal lobe suggests a distinction between adjacent MTL cortices and hippocampus

    Hippocampus 22:2290–2302.

    https://doi.org/10.1002/hipo.22047

    • Google Scholar
33. 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

    • Google Scholar
34. 1. Litman L
    2. Awipi T
    3. Davachi L

    (2009) Category-specificity in the human medial temporal lobe cortex

    Hippocampus 19:308–319.

    https://doi.org/10.1002/hipo.20515

    • Google Scholar
35. 1. Maass A
    2. Berron D
    3. Libby L
    4. Ranganath C
    5. Duzel E

    (2015)

    Functional subregions of the human entorhinal cortex

    e06426, eLife, 4, 10.7554/eLife.06426.

    • Google Scholar
36. 1. Martin CB
    2. McLean DA
    3. O'Neil EB
    4. Köhler S

    (2013) Distinct familiarity-based response patterns for faces and buildings in perirhinal and parahippocampal cortex

    The Journal of Neuroscience 33:10915–10923.

    https://doi.org/10.1523/JNEUROSCI.0126-13.2013

    • Google Scholar
37. 1. Moser EI
    2. Moser M-B

    (2013) Grid cells and neural coding in high-end cortices

    Neuron 80:765–774.

    https://doi.org/10.1016/j.neuron.2013.09.043

    • Google Scholar
38. 1. Munoz M
    2. Insausti R

    (2005) Cortical efferents of the entorhinal cortex and the adjacent parahippocampal region in the monkey (Macaca fascicularis)

    The European Journal of Neuroscience 22:1368–1388.

    https://doi.org/10.1111/j.1460-9568.2005.04299.x

    • Google Scholar
39. 1. Poser BA
    2. Koopmans PJ
    3. Witzel T
    4. Wald LL
    5. Barth M

    (2010) Three dimensional echo-planar imaging at 7 Tesla

    Neuroimage 51:261–266.

    https://doi.org/10.1016/j.neuroimage.2010.01.108

    • Google Scholar
40. 1. Power JD
    2. Barnes KA
    3. Snyder AZ
    4. Schlaggar BL
    5. Petersen SE

    (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

    Neuroimage 59:2142–2154.

    https://doi.org/10.1016/j.neuroimage.2011.10.018

    • Google Scholar
41. 1. Power JD
    2. Schlaggar BL
    3. Petersen SE

    (2015) Recent progress and outstanding issues in motion correction in resting state fMRI

    Neuroimage 105:536–551.

    https://doi.org/10.1016/j.neuroimage.2014.10.044

    • Google Scholar
42. 1. Pruim RH
    2. Mennes M
    3. Buitelaar JK
    4. Beckmann CF

    (2015a) Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting-state fMRI

    Neuroimage 112:278–287.

    https://doi.org/10.1016/j.neuroimage.2015.02.063

    • Google Scholar
43. 1. Pruim RH
    2. Mennes M
    3. van Rooij D
    4. Llera Arenas A
    5. Buitelaar JK
    6. Beckmann CF

    (2015b) ICA-AROMA: a robust ICA-based strategy for removing motion artifact from fMRI data

    Neuroimage 112:267–277.

    https://doi.org/10.1016/j.neuroimage.2015.02.064

    • Google Scholar
44. 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

    • Google Scholar
45. 1. Reagh ZM
    2. Yassa MA

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

    Proceedings of the National Academy of Sciences of USA 111:E4264–E4273.

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

    • Google Scholar
46. 1. Sargolini F
    2. Fyhn M
    3. Hafting T
    4. McNaughton BL
    5. Witter MP
    6. Moser M-B
    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

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

    (2012) Direct evidence for Domain-sensitive functional subregions in human entorhinal cortex

    The Journal of Neuroscience 32:4716–4723.

    https://doi.org/10.1523/JNEUROSCI.5126-11.2012

    • Google Scholar
48. 1. Smith SM
    2. Beckmann CF
    3. Andersson J
    4. Auerbach EJ
    5. Bijsterbosch J
    6. Douaud G
    7. Duff E
    8. Feinberg DA
    9. Griffanti L
    10. Harms MP
    11. Kelly M
    12. Laumann T
    13. Miller KL
    14. Moeller S
    15. Petersen S
    16. Power J
    17. Salimi-Khorshidi G
    18. Snyder AZ
    19. Vu AT
    20. Woolrich MW
    21. Xu J
    22. Yacoub E
    23. Uğurbil K
    24. Van Essen DC
    25. Glasser MF
    26. WU-Minn HCP Consortium

    (2013) Resting-state fMRI in the Human Connectome Project

    Neuroimage 80:144–168.

    https://doi.org/10.1016/j.neuroimage.2013.05.039

    • Google Scholar
49. 1. Staresina BP
    2. Duncan KD
    3. Davachi L

    (2011) Perirhinal and parahippocampal cortices differentially contribute to later recollection of object- and scene-related event details

    The Journal of Neuroscience 31:8739–8747.

    https://doi.org/10.1523/JNEUROSCI.4978-10.2011

    • Google Scholar
50. 1. Suzuki WA
    2. Amaral DG

    (1994)

    Topographic organization of the reciprocal connections between the monkey entorhinal cortex and the perirhinal and parahippocampal cortices

    The Journal of Neuroscience 14:1856–1877.

    • Google Scholar
51. 1. Tsao A
    2. Moser M-B
    3. Moser EI

    (2013) Traces of Experience in the lateral entorhinal cortex

    Current Biology 23:399–405.

    https://doi.org/10.1016/j.cub.2013.01.036

    • Google Scholar
52. 1. Van Essen DC

    (1985)

    Functional organization of primate visual cortex

    Cerebral Cortex 3:259–329.

    • Google Scholar
53. 1. Van Essen DC
    2. Smith SM
    3. Barch DM
    4. Behrens TE
    5. Yacoub E
    6. Ugurbil K
    7. WU-Minn HCP Consortium

    (2013) The WU-Minn human connectome project: an overview

    Neuroimage 80:62–79.

    https://doi.org/10.1016/j.neuroimage.2013.05.041

    • Google Scholar
54. 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

    • Google Scholar
55. 1. Vilberg KL
    2. Davachi L

    (2013) Perirhinal-hippocampal connectivity during reactivation is a marker for object-based memory consolidation

    Neuron 79:1232–1242.

    https://doi.org/10.1016/j.neuron.2013.07.013

    • Google Scholar
56. 1. Wang JX
    2. Rogers LM
    3. Gross EZ
    4. Ryals AJ
    5. Dokucu ME
    6. Brandstatt KL
    7. Hermiller MS
    8. Voss JL

    (2014) Targeted enhancement of cortical-hippocampal brain networks and associative memory

    Science 345:1054–1057.

    https://doi.org/10.1126/science.1252900

    • Google Scholar
57. 1. Willenbockel V
    2. Sadr J
    3. Fiset D
    4. Horne GO
    5. Gosselin F
    6. Tanaka JW

    (2010) Controlling low-level image properties: the SHINE toolbox

    Behavior Research Methods 42:671–684.

    https://doi.org/10.3758/BRM.42.3.671

    • Google Scholar
58. 1. Witter MP
    2. Groenewegen HJ
    3. Lopes da Silva FH
    4. Lohman AH

    (1989) Functional organization of the extrinsic and intrinsic circuitry of the parahippocampal region

    Progress in Neurobiology 33:161–253.

    https://doi.org/10.1016/0301-0082(89)90009-9

    • Google Scholar
59. 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

    • Google Scholar

Author details

1. Tobias Navarro Schröder

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   TNS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Koen V Haak

   For correspondence

   t.navarroschroeder@donders.ru.nl

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-6498-1846

2. Koen V Haak

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   KVH, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Tobias Navarro Schröder

   Competing interests

   The authors declare that no competing interests exist.

3. Nestor I Zaragoza Jimenez

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   NIZJ, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Christian F Beckmann

   1. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands
   3. Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom

   Contribution

   CFB, Conception and design, Drafting or revising the article

   Contributed equally with

   Christian F Doeller

   Competing interests

   The authors declare that no competing interests exist.

5. Christian F Doeller

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   CFD, Conception and design, Drafting or revising the article

   Contributed equally with

   Christian F Beckmann

   For correspondence

   christian.doeller@donders.ru.nl

   Competing interests

   The authors declare that no competing interests exist.

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