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Functional topography of the human entorhinal cortex

  1. Tobias Navarro Schröder Is a corresponding author
  2. Koen V Haak
  3. Nestor I Zaragoza Jimenez
  4. Christian F Beckmann
  5. Christian F Doeller Is a corresponding author
  1. Radboud University, Netherlands
  2. Radboud University Medical Centre, Netherlands
  3. University of Oxford, United Kingdom
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Cite as: eLife 2015;4:e06738 doi: 10.7554/eLife.06738

Abstract

Despite extensive research on the role of the rodent medial and lateral entorhinal cortex (MEC/LEC) in spatial navigation, memory and related disease, their human homologues remain elusive. Here, we combine high-field functional magnetic resonance imaging at 7 T with novel data-driven and model-based analyses to identify corresponding subregions in humans based on the well-known global connectivity fingerprints in rodents and sensitivity to spatial and non-spatial information. We provide evidence for a functional division primarily along the anteroposterior axis. Localising the human homologue of the rodent MEC and LEC has important implications for translating studies on the hippocampo-entorhinal memory system from rodents to humans.

https://doi.org/10.7554/eLife.06738.001

eLife digest

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.

https://doi.org/10.7554/eLife.06738.002

Introduction

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.

Results

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).

Table 1
Selection of regions associated with the posterior-medial (PM) and the anterior-temporal (AT) system (Libby et al., 2012).

The coordinates of the PM system reflect peak voxel coordinates of a seed-based connectivity contrast of right parahippocampal cortex > right perirhinal cortex connectivity reported by Libby et al. (Libby et al., 2012). The coordinates of the AT system reflect peak voxel coordinates of a seed-based connectivity contrast of right perirhinal cortex > right parahippocampal cortex connectivity. Coordinates are in MNI space.

https://doi.org/10.7554/eLife.06738.003
Left hemisphereLeft hemisphere
xyzxyz
PM System
 Medial posterior occipital cortex (BA 18)14−728
 Occipital pole (BA 17)−16−9622
 Parahippocampal cortex−12−42−822−32−8
 Posterior cingulate cortex (BA 29)−4−46410−4410
 Posterior hippocampus−20−30−218−360
 Posterior thalamus−20−34022−306
 Retrosplenial cortex (BA 30)−16−52−422−460
AT System
 Dorsolateral prefrontal cortex (BA 9)−246024185824
 Dorsomedial prefrontal cortex (BA 8)−2−6034
 Frontal polar cortex (BA 10)4060−2
 Lateral precentral gyrus (BA 6)54410
 Medial prefrontal cortex (BA 8)−2−6034
 Orbitofrontal cortex (BA 11/47)−616−22822−20
 Postcentral gyrus (BA 4)62−1016
 Posterior superior temporal gyrus (BA 22)−62−3414
 Rostrolateral prefrontal cortex (BA 10)3860−12
 Temporal polar cortex (BA 38)3422−36
 Ventrolateral prefrontal cortex (BA 44/45)−56618
Figure 1 with 1 supplement see all
Subdivisions of entorhinal cortex (EC) and connectivity to anterior-temporal (AT) and posterior-medial (PM) cortical networks.

(A) Schematic of the AT and PM system. Spherical regions-of-interest (ROIs) were centred on MNI coordinates associated with either of the two systems (Libby et al., 2012), normalised to the group-specific template of the navigation study and then masked to include only gray matter voxels. The AT system included medial-prefrontal and orbitofrontal regions, whereas the PM system included occipital and posterior-parietal regions, see Table 1 for all selected regions. (B) Right parasagittal slice showing voxel-wise seed-based connectivity of the PM system restricted to the EC. Note the PM peak. (C) Right parasagittal slice showing voxel-wise seed-based connectivity of the AT system restricted to the EC. Note a peak in the anterior-lateral EC. (D) ROI-based connectivity estimates. Left panel: Connectivity strength (partial correlation coefficient) of anterior (left) and posterior EC (right) is plotted separately for the AT system (red) and the PM system (blue). The systems differ in their entorhinal connectivity: the anterior EC connects stronger to the AT compared to the PM network. Right panel: Connectivity strength with lateral (left) and posterior EC (right) is plotted separately for the AT system (red) and the PM system (blue). Lateral EC connected stronger to the AT compared to the PM network. Error bars show S.E.M. over subjects. See Figure 1—figure supplement 1 for additional slices.

https://doi.org/10.7554/eLife.06738.004

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).

Figure 2 with 4 supplements see all
Dominant mode of functional connectivity change within EC and sensitivity to spatial and non-spatial information.

(A) Dominant mode of functional connectivity change at the group-level (Spearman's R = 0.53). Similar colours indicate similar connectivity with the rest of the brain. (B) 3D rendering of the two clusters derived from the dominant mode of functional connectivity change (displayed in red and blue) and the outlines of the group-specific template. Upper panel: right side view. Lower panel: top view (see Figure 2—figure supplement 1 for coronal views of the two clusters). (C) Upper panel: Map shows results of a non-parametric randomisation test of the spatial and non-spatial stimulation experiment restricted to the EC for display purposes (see Figure 2—figure supplement 2 for whole-brain maps). The ‘scenes > objects’ contrast is displayed in blue to light-blue, the ‘scenes < objects’ contrast in red to yellow. Note that voxels in pmEC are sensitive to scenes, whereas voxels in alEC are sensitive to objects. Lower panel: The clusters from panel B exhibit antagonistic responses to spatial and non-spatial stimuli. Beta estimates for the contrast ‘scenes > objects’ (averaged across participants) are shown for clusters A and B. T(20) = 4.9, p = 0.0001. Error bars show S.E.M. over participants. (D) Whole-volume functional connectivity with clusters A and B. Regions connecting more with cluster A (p < 0.05, FWE corrected), such as occipital and posterior-parietal cortex that form part of the PM system are shown in blue. Regions connecting more with cluster B (p < 0.05, FWE corrected), such as medial-prefrontal and orbitofrontal cortex which form part of the AT system are displayed in red.

https://doi.org/10.7554/eLife.06738.006
Figure 3 with 1 supplement see all
Second-dominant mode of functional connectivity change within EC and sensitivity to spatial and non-spatial information.

(A) Second-dominant mode of functional connectivity change at the group-level (Spearman's R = 0.28). Similar colours indicate similar connectivity with the rest of the brain. (B) 3D rendering of the two clusters derived from the second-dominant mode of functional connectivity change (displayed in red and blue) and the outlines of the group-specific template. Upper panel: right side view. Lower panel: top view (see Figure 3—figure supplement 1 for coronal views of the two clusters). (C) The clusters shown in panel B exhibit no antagonistic responses to spatial and non-spatial stimuli. Beta estimates for the contrast ‘scenes > objects’ (averaged across participants) are shown for cluster A and B (T(20) = −0.26, p = 0.8). Error bars show S.E.M. over participants. (D) Regions connecting more with cluster A (p < 0.05, FWE corrected) are shown in blue. Regions connecting more with cluster B (p < 0.05, FWE corrected) are shown in red. Cluster A connected more with most of the neocortex.

https://doi.org/10.7554/eLife.06738.011
Dominant and second-dominant modes of functional connectivity change on the basis of resting-state functional magnetic resonance imaging.

Results of analysis of the first 60 participants of the WU-Minn Human Connectome Project (HCP), acquired on two different days (Smith et al., 2013). Top row: day one. Bottom row: day two. (A, C) The dominant mode of functional connectivity change follows an anteroposterior trajectory. (B, D). The second-dominant mode of functional connectivity change follows a mediolateral trajectory. Both modes were highly reproducible across different scanning days (dominant mode: Pearson's R = 0.99 p < 0.001, second-dominant mode: R = 0.98; p < 0.001). Topology preservation—dominant mode, day one: Spearman's R = 0.62; day two: R = 0.61; second-dominant mode, day one: R = 0.44; day two: R = 0.46.

https://doi.org/10.7554/eLife.06738.013

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
3D rendering of the two clusters derived using the dominant mode of functional connectivity change. Cluster A is shown in red and cluster B in blue.
https://doi.org/10.7554/eLife.06738.014

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.

Discussion

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).

Materials and methods

Participants

Navigation experiment

26 participants took part in the study (11 females, age 19–36, mean 23 years). 22 entered the analysis and 4 were excluded due to excessive movement (number of instantaneous movements [Power et al., 2012] > 0.5 mm exceeded the mean plus 1 standard deviation). Materials and methods were approved by the local research ethics committee (CMO University Duisburg-Essen, Germany and CMO region Arnhem-Nijmegen, NL). Written informed consent was obtained from each participant for data analysis and publication of the study results.

Resting-state dataset

The resting-state dataset consisted of the first 60 participants (41 females, age ranges: 4 between 22 and 25 years, 23 between 26 and 30 years and 33 between 31 and 35 years) of the WU-Minn Human Connectome Project (HCP) (Van Essen et al., 2013). The experiments were performed in accordance with relevant guidelines and regulations and all experimental protocol was approved by the Institutional Review Board (IRB) (IRB # 201204036; Title: ‘Mapping the Human Connectome: Structure, Function, and Heritability’). Written informed consent was obtained from each participant for data analysis and publication of the study results.

Spatial and non-spatial stimulation

21 participants (13 females, age 20–54, mean age 26 years) participated in the experiment. None of the participants participated in more than one experiment. Materials and methods were approved by the local research ethics committee (CMO University Duisburg-Essen, Germany and CMO region Arnhem-Nijmegen, NL). Written informed consent was obtained from each participant for data analysis and publication of the study results.

FMRI acquisition

Navigation experiment

Blood-oxygenation-level-dependent T2*-weighted functional images were acquired on a 7 T Siemens MAGNETOM scanner (Siemens Healthcare, Erlangen, Germany) using a three dimensional echo-planar imaging (3D EPI) pulse sequence (Poser et al., 2010) on a 32-channel surface coil: TR = 2.7 s, TE = 20 ms, flip angle = 14°, slice thickness = 0.92 mm, slice oversampling = 8.3%, in-plane resolution = 0.9^2 mm, field of view (FoV) = 210 mm in each direction, 96 slices, phase encoding acceleration factor = 4, 3D acceleration factor = 2. The first five volumes of the main scan were discarded to allow for T1 equilibration. A field map using a gradient echo sequence was recorded for distortion correction of the acquired EPI images.

Resting-state dataset

Participants underwent four resting-state scanning sessions recorded on two days, each comprising 15 min multi-band accelerated (TR = 0.72 s) fMRI. The two scans from each day were concatenated into scans of 30 min. Each day one scan had a left-right phase encoding direction and the other a right-left phase encoding direction and concatenating the two scans allowed for correcting the ensuing opposing field inhomogeneities. Whole-brain images were acquired at 2 mm isotropic resolution and all data are publicly available. For details see Van Essen et al (Van Essen et al., 2013; Smith et al., 2013).

Spatial and non-spatial stimulation

The same 7 T Siemens MAGNETOM scanner was used as for the navigation experiment. Procedures and acquisition parameters were the same unless described otherwise. TR = 2.1 s, TE = 25 ms, flip angle = 13°, slice oversampling = 10%, in-plane resolution = 1.5^2 mm, FoV = 224 mm in each direction, 80 slices, Phase Encoding acceleration factor = 3, 3D acceleration factor = 2. To allow for T1 equilibration, three (N = 11) or seven (N = 5) dummy volumes were discarded before the main scan.

Experimental tasks

Navigation experiment

Participants performed an object-location memory task while freely navigating a 3D, virtual environment task adapted from Doeller et al. (Doeller et al, 2008; Doeller et al., 2010) (Figure 5A). In this self-paced task participants collected and replaced six everyday objects within a virtual arena. They collected each object from its associated location once during an initial trial, by running over it. In each subsequent trial they saw an image (cue) of one of the objects in the upper part of the screen and had to move to the object's associated location and press a button (replace phase). After this response, the object appeared in its associated position and participants collected it again (feedback phase). After an average of 3 trials (range 2–4 trials), a fixation cross on a grey background was presented for 4 s (inter-trial-interval, ITI). Next to the spatial aspects of the navigation task, non-spatial task components were present approximately half of the time. Therefore, the task engaged functions associated with both the PM and the AT network, respectively. Each scanning session was subdivided into EPI acquisition blocks of 210 volumes. Participants underwent an average of 5 blocks (±1). Task-related effects from the navigation experiment are subject of another report.

Cognitive tasks.

(A) Navigation experiment. First person view of the virtual arena that participants navigated freely to perform the object-location memory task. (B) Spatial and non-spatial stimulation. Left: An example stimulus of a ‘non-spatial’ object. Right: An example stimulus of a ‘spatial’ scene.

https://doi.org/10.7554/eLife.06738.015

Spatial and non-spatial stimulation

Participants were presented with pictures of spatial and non-spatial stimuli (see Figure 5B) and gave a trial-by-trial animacy judgement. Images of objects (non-spatial) and 12 images of scenes (spatial)—as well as 24 images of two categories of animate stimuli not considered in the analyses—were resized to 400 × 400 pixels and shown in grey shading and matched on their low-level features (luminance, contrast and spatial frequency) through the use of the SHINE toolbox (Spectrum, Histogram and Intensity Normalization and Equalization [Willenbockel et al., 2010]). Single stimuli were presented for 2 s followed by an ITI of variable duration (mean 5 s, range 1.5–10 s). The scanning sessions were subdivided into two runs of four blocks each, with a duration of approximately 27 min in duration per run.

Data pre-processing

Navigation experiment

Data pre-processing was implemented through the use of the automatic analysis library (Cusack et al., 2015) (http://automaticanalysis.org/). Pre-processing included motion correction in SPM8 (http://www.fil.ion.ucl.ac.uk/spm), data de-noising with the FIX artifact removal procedure implemented in FSL 5.0.4 (http://fsl.fmrib.ox.ac.uk/fsl/) that has been trained manually on 10 of the 22 participants and nonlinear normalisation to a group-specific EPI template with the Advanced Neuroimaging Toolbox (Avants et al., 2011) (ANTS; http://www.picsl.upenn.edu/ANTS/). The use of a group-specific EPI template was feasible because of the anatomical detail provided by the sub-millimetre images. In addition, this rendered the registration to structural images unnecessary, which poses an additional source of noise because of regional differences in distortion of the 7 Tesla EPIs. The functional data were smoothed with a full-width at half-maximum (FWHM) Gaussian kernel of 2.5 mm3 (roughly 2.5 times the voxel size) to increase the SNR and improve ICA-based data de-noising with the FIX procedure (see above) while maintaining high spatial resolution for connectivity analyses. Correction for residual motion artefacts was performed with an ICA-based method, which has been shown to outperform motion scrubbing and spike-regression methods both in terms of reproducibility of resting-state networks and conservation of temporal degrees of freedom (Pruim et al., 2015a; Pruim et al., 2015b). In addition, brain extraction, tissue segmentation and high-pass filtering with a 128-s cut-off were carried out with FSL.

Resting-state dataset

The publicly available data were pre-processed as described in Smith et al. (2013), which included correction for spatial distortions and head motion, registration to the T1 weighted structural image, resampling to 2 mm Montreal Neurological Institute (MNI) space, global intensity normalization, high-pass filtering with a cut-off at 2000s, and the FIX artefact removal procedure. Additionally, we smoothed the images with a 6 mm3 FWHM kernel and converted the time-series of each voxel to percent signal change by subtracting and dividing by their mean amplitude value over time.

Spatial and non-spatial stimulation

The same pre-processing procedure was used as for the navigation experiment, unless described otherwise below. Since the voxel size was larger and only univariate analyses were conducted, we did not perform data de-noising with the FIX procedure. The functional data were smoothed with a FWHM kernel of 5 mm3 (roughly 2.5 times the voxel size) to increase the SNR.

ROI definitions

For the 7 Tesla experiments (navigation experiment and spatial and non-spatial stimulation experiment), an EC ROI was manually defined with ITK Snap 3.2 (Yushkevich et al., 2006) (http://www.itksnap.org) on the group-specific high-resolution mean EPI template from the navigation experiment. Manual segmentation on the group-specific EPI template circumvented registration problems between structural images and partly distorted functional images. Based on anatomical landmarks as described by Insausti et al., (1998) and Frankó et al., (2014), the posterior border of the EC was set to ∼1.5 mm posterior to the gyrus intralimbicus, the anterior border to ∼1.5 mm posterior to the limen insulae, the lateral border to the midpoint of the medial bank of the collateral sulcus and the medial border to the hippocampal fissure at the level of the uncus. A medial EC and a lateral EC ROI of roughly equal volume were created by dividing the main ROI mediolaterally (which roughly corresponded to a proximo-distal division in relation to the hippocampus) on consecutive coronal slices along the entire anteroposterior axis. An anterior EC and a posterior EC ROI of roughly equal volume were created by dividing the main ROI mid-way between the most anterior and the most posterior coronal slice of the EC.

For the ‘model-based’ analysis, one ROI mask was created for the PM system and another one for the AT system. For this purpose we placed spheres of 4 mm radius on MNI coordinates associated with each system by Libby et al., (Libby et al., 2012) (see Table 1 for the selected coordinates). For the control analysis using the resting-state fMRI data from the Human Connectome Project, a probabilistic entorhinal ROI was generated using the Freesurfer toolbox (Augustinack et al., 2013). Freesurfer's cortical reconstruction algorithm (‘recon-all’) was used on a T1 weighted MNI template image to generate an ROI mask of the EC with a probability threshold of 90% that was binarized and converted to NiFTI file format.

Seed-based connectivity analysis

For the quantification of the model-based analyses of connectivity between the AT and PM networks and the manually segmented medial and lateral or anterior and posterior EC, singular-value decomposition (SVD) and subtraction of the mean was performed on the voxel-wise time-series within each ROI. Next, functional connectivity was estimated by means of partial correlation analysis between the time-series of groups of ROIs. Finally, Pearson's partial correlation coefficients were Fisher-Z transformed and used for across-subject comparisons (Figure 1D). For the estimation of voxel-wise connectivity maps we used the ‘dual_regression’ function implemented in FSL 5.0.4 (http://fsl.fmrib.ox.ac.uk/fsl/). This involved using the time-series of pairs of seed regions as regressors in a GLM to estimate functional connectivity to voxels in a target region (Figure 1B, Figure 1C and Figure 1—figure supplement 1) or to the rest of the brain (Figure 2D and Figure 3D). Finally, group-level statistics were computed with non-parametric randomisation tests (FSL 5.0.4, http://fsl.fmrib.ox.ac.uk/fsl/) and threshold-free cluster enhancement for null-hypothesis testing and the computation of p-value maps (Figure 2D and Figure 3D). All connectivity analyses were restricted to gray matter voxels.

Data-driven connectivity analysis

To overcome limitations of the seed-based connectivity analysis, we employed ConGrads (Haak et al., 2014), which allows for tracing the dominant modes of functional connectivity change within a pre-specified region of the brain in a fully data-driven manner. First, the fMRI time series from the EC were rearranged into a time-by-voxels matrix, which was also done for the fMRI time series of all gray-matter voxels outside the EC. For reasons of stability and computational tractability, we losslessly reduced the dimensionality of the data outside the EC using SVD. We determined the connectivity fingerprints of each voxel inside the EC by computing the correlation between the voxel-wise time series and the SVD-transformed data, and then used the η2 coefficient to quantify the similarities among the voxel-wise fingerprints (Cohen et al., 2008). Next, we fed the ensuing similarity-matrix to the LE algorithm (Belkin and Niyogi, 2003), resulting in a series of vectors that represent the dominant modes of functional connectivity change. The LE algorithm and variants thereof have previously been successfully applied to trace changes in probabilistic tractography connectivity (Johansen-Berg et al., 2004; Cerliani et al., 2012), while in the context of resting-state fMRI, ConGrads has been shown to generate highly reproducible results in regions such as the human motor strip, both across sessions and participants (Haak et al., 2014). Note that group-level results were obtained by running the LE algorithm on the average of the individual similarity matrices and that the analysis was performed separately for left and right hemispheric EC ROIs.

To quantify how well the ensuing modes of connectivity change preserved the order of the similarities among the original, high-dimensional connectivity fingerprints (topology preservation) we used Spearman's rank correlation coefficient. Because the LE algorithm maximizes topology preservation, we report the correlation coefficients without p-values.

Clustering was performed on the high-resolution data from the navigation experiment by grouping voxels above and below the median value of each mode of functional connectivity change, which resulted in two equally sized clusters per mode of connectivity change. For the ROI analyses on the data of the spatial and non-spatial stimulation experiment, the clusters were warped into MNI space.

SNR estimation

tSNR was determined by dividing the mean signal within a region by the standard deviation of that signal over time. Conversely, spatial SNR was determined by dividing the mean signal within a region by the standard deviation of the signal across voxels. This was done for each time point and spatial SNR was then averaged over time.

Motion scrubbing

First, we determined time points where instantaneous movement (Power et al., 2012, 2015) exceeded a threshold of 0.5 mm. Then we excluded these time points (volumes) including the one preceding and the two thereafter from subsequent analyses. Hence, per instantaneous movement above threshold, four volumes were removed. On average this resulted in 121 volumes being removed from participant's time-series (range: 4–300).

Univariate fMRI analyses

The data from the spatial and non-spatial stimulation experiment were analysed in native (subject-specific) space with general linear models that included regressors of interest for object and scene trials. Six movement regressors were included to account for movement-related noise. The regressors were convolved with the canonical hemodynamic response function and fitted to the time-series at each voxel in a whole-brain analysis. Single-subject contrast images were first normalised to a group-specific template and then to the MNI space. Group-level statistics were computed with non-parametric randomisation tests (FSL 5.0.4, http://fsl.fmrib.ox.ac.uk/fsl/) on the contrast images (objects vs scenes and scenes vs objects) using variance smoothing with a 5 mm3 kernel—to improve the estimation of the variance that feeds into the final t-statistic.

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Decision letter

  1. Howard Eichenbaum
    Reviewing Editor; Boston University, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Functional topography of the human entorhinal cortex” for consideration at eLife. Your article has been favorably evaluated by Eve Marder (Senior editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors.

The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

In this well written and interesting study, the authors used 7 Tesla ultra-high field functional magnetic resonance imaging to identify functional subdivisions of the human EC through the analysis of preferred connectivity with parahippocampal and perirhinal cortices. Aside from a solidly supported hypothesis on how connectional patterns might allow the definition of functionally different domains in the human entorhinal cortex, the authors addressed intrinsic connectivity of the EC. This study, as well as its counterpart study by Maass et al., should be welcomed and heralded as an important step forward to our understanding of how to functionally divide EC in humans.

1) Like its counterpart paper, the study falls into a well-known trap of comparative anatomical studies in that chosen nomenclatures are taken to have implications that have never been intended, neither implicit nor explicit. How to define an area in the brain is one problem, how to find the homologue of an area defined in one species in a second species is another question. The authors take an originally cytoarchitectonically defined subdivision of EC into LEC and MEC as the starting point, adding connectivity patterns as the defining second criterion. They then look for connection patterns in the human and find two regions as well. That supports the conclusion that the functional homologues of LEC and MEC can be defined in humans based on the data available (Discussion). In the same sentence it is clearly stated that the LEC homologue is situated anterolateral, while the MEC homologue is to be found at a posteromedial position. Unfortunately, then the trap snaps; the authors then claim that this position is different from that in the rodent since LEC means lateral and MEC means medial. Although they are correct that the given names indicate a position, this is a rather unfortunate interpretation/nomenclature, because in the rodent and in several other species MEC has a posteromedial preferred position and LEC an anterolateral. Maybe a bit less extreme as in the monkey and human, but not all that different. Several papers on the primate brain, including both monkey and human data, unfortunately make the same mistake and assume that all that has a lateral position in EC is LEC and all with a medial position is MEC. The authors correctly conclude that this is likely incorrect, but do not make this explicit and therefore add to the confusion.

A few examples of sentences that add to the confusion instead of clarifying it:

In the Introduction: “Furthermore, the two subregions differ not only in structure and function, but also in their vulnerability to Alzheimer's disease-related pathology (Khan et al., 2013)”. In Khan et al. 2013, the authors indeed use LEC to describe AD pathology, but they actually describe pathology in a longitudinal strip of EC that likely encompasses both LEC and MEC, both when one looks at cytoarchitectonics as well as monkey connectivity studies.

In the Results section: “Based on studies in rodents, the two networks are hypothesised to connect to either the LEC or the MEC, respectively. Studies in non-human primates, however, predict that the two systems project to the anterior and posterior EC, respectively. In order to test both of these predictions…” Here the authors implicitly assume that LEC and MEC are two entities different from alEC and pmEC.

In the Discussion: “Here, we show that a simple correspondence of MEC/LEC to the mediolateral axis in humans is not valid.” This sentence, although literally true, implicitly contradicts the authors' own concluding statement that the functional homologue can be determined based on the outcome.

Also in the Discussion: “Previous neuroimaging studies in humans reported differences between medial and lateral entorhinal cortex that mimicked differences between the rodent MEC and LEC. 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.” Again, medial and lateral are here used as pure topological indicators, not as functional, hodological or cytoarchitural definition, so using LEC and MEC is confusing.

Therefore, we strongly suggest that the authors start off with defining areas as alEC and pmEC, and state that other human studies used LEC and MEC to indicate lateral and medial strips of EC. In the remainder of the manuscript they should only use LEC and MEC when is clear that these are defined, not just indicating a position. This would lead and support their statement in the Discussion: “However, the anatomical organisation and connectivity patterns are largely conserved across species and the characteristic projections from the PRC and PHC rather map on the anteroposterior axis in primates.”

2) The paper can be improved by adding a few simple analyses that can hopefully illustrate the results in a clear manner. Below are potential technical issues that the authors should address. These issues, described in more detail below, should be straightforward to deal with in the revision:

A) The study elegantly demonstrates that pmEC and alEC show different patterns of whole-brain connectivity with extended posterior medial (PM) and anterior temporal (AT) neocortical networks. If we understand the methods, the authors used a single ROI for the entire PM network and one for the entire AT network. At least for illustrative purposes, it would be useful to present separate results for each sphere and each EC ROI. Ideally they could show a correlation matrix with the 4 EC ROIs (well, actually 8, as described in my next point) in columns and the individual PM and then AT ROI spheres in rows. Hopefully, on a quick scan of the matrix, readers will be able to see differences in the connectivity profiles of the anterior lateral and posterior medial EC ROIs.

B) I did not see any mention of laterality. I might have missed it, but if the authors collapsed across hemispheres, I'd like them to consider also presenting results with separate left and right hemisphere ROIs. Doing so would provide a natural internal replication, because one would expect more extensive contralateral connectivity within the alEC regions and within the pmEC regions than between alEC and pmEC.

C) Along the same lines as the first 2 points, it appears that the first set of a priori ROI analyses separately examine medial and lateral EC ROIs and anterior and posterior EC ROIs. If the authors planned to investigate both axes, wouldn't it make sense to include all 4 ROIs in a single factorial analysis with anterior-posterior and medial-lateral factors? If the analysis revealed an interaction, it would strengthen the case that the functional organization of the EC cuts across both axes.

3) It might also help to present whole-brain maps (preferably on a surface rendering) for the contrast between anterior-lateral and posterior-medial ROI connectivity, so that readers can see which regions show differential connectivity. Alternatively, it might be preferable to show single-region connectivity maps.

4A) In general, motion artifact is always a concern with functional connectivity analyses (summarized in Power et al., 2015), and more so when one considers that the EC is typically a site with significant signal dropout. It is surprising that these issues are not raised in the paper. We would like to know whether the pmEC and alEC (either defined arbitrarily or using the ConGrads approach) have different spatial or temporal SNR values.

B) Also, the authors used ICA to deal with residual motion artifact, but it is not clear that ICA will adequately remove the effects of transient motion spikes while preserving the rest of the timeseries. It would be reassuring if the authors can replicate the results of at least one of the functional connectivity analyses by using motion scrubbing/censoring instead of ICA correction (see Power et al., 2015).

5A) The ConGrads analysis is not clear. Giving the authors the benefit of the doubt (more so if they can present the simple analyses suggested above), but a better and simpler explanation of this method is needed. It sounds like a promising approach, but given that the method is not in widespread use, readers may be put off by the brief explanation.

B) As a related issue, the meaning of the two “connectopies” was difficult to figure out. It sounds like what the authors are saying is that there are two connectivity gradients, one anterior-posterior, and one that is lateral-medial. If so, it would be nice for the authors to spell this out, and to simplify the corresponding caption text for Figure 2 and Figure 3. Also, if the algorithm is pulling out two connectivity gradients, could the relationship be more simply and intuitively captured by a single al-to-pm gradient. If it does not distort the actual patterns in the data, the authors should consider somehow merging the two connectopies to create simple alEC and pmEC ROIs. At present, it is difficult for the reader to look at the figures and see a figure that convincingly depicts the functional topography that is described in the text.

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

Thank you for resubmitting your work entitled “Functional topography of the human entorhinal cortex” for further consideration at eLife. Your revised article has been favorably evaluated by Eve Marder (Senior editor), a Reviewing editor, and one of the original reviewers. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below.

In the revised manuscript, the authors have carefully addressed the comments about potential confusion in nomenclature when comparing the human brain to the non-human primate and rodent brain, in particular with respect to the subdivisions of EC. The manuscript has improved significantly, however there are still two remaining issues.

First, the authors suggested to change their initially chosen nomenclature from anterolateral to posteromedial EC into temporal to septal EC. Though understandable, the choice is very counterintuitive, since the septotemporal nomenclature is typically used for the non-primate hippocampus where one tip of the hippocampus is topologically associated with the septal complex. This is not the case in the primate, and therefore, most authors elected to use an anterior-posterior nomenclature. I strongly urge the authors to maintain this convention, irrespective of their valid argument that the AP axis does not completely reflect the tilted orientation of the hippocampal and parahippocampal regions in the human. From what is said in the Results, it is clear that the authors seem ambivalent about their newly introduced nomenclature.

In addition, in view of the fact that this study nicely complements another study on subdivisions of the human EC by Maass et al., this provides a unique and important opportunity to establish a consistent human terminology for the entorhinal cortex. To really imprint a new terminology, this is the likely one and only chance to do so; it would be great if the two papers concur on a common terminology. Knowing the two author groups are in touch, the implementation of a common terminology should be feasible and highly welcome.

https://doi.org/10.7554/eLife.06738.016

Author response

1) Like its counterpart paper, the study falls into a well-known trap of comparative anatomical studies in that chosen nomenclatures are taken to have implications that have never been intended, neither implicit nor explicit. How to define an area in the brain is one problem, how to find the homologue of an area defined in one species in a second species is another question. The authors take an originally cytoarchitectonically defined subdivision of EC into LEC and MEC as the starting point, adding connectivity patterns as the defining second criterion. They then look for connection patterns in the human and find two regions as well. That supports the conclusion that the functional homologues of LEC and MEC can be defined in humans based on the data available (Discussion). In the same sentence it is clearly stated that the LEC homologue is situated anterolateral, while the MEC homologue is to be found at a posteromedial position. Unfortunately, then the trap snaps; the authors then claim that this position is different from that in the rodent since LEC means lateral and MEC means medial. Although they are correct that the given names indicate a position, this is a rather unfortunate interpretation/nomenclature, because in the rodent and in several other species MEC has a posteromedial preferred position and LEC an anterolateral. Maybe a bit less extreme as in the monkey and human, but not all that different. Several papers on the primate brain, including both monkey and human data, unfortunately make the same mistake and assume that all that has a lateral position in EC is LEC and all with a medial position is MEC. The authors correctly conclude that this is likely incorrect, but do not make this explicit and therefore add to the confusion.

[…] Therefore, we strongly suggest that the authors start off with defining areas as alEC and pmEC, and state that other human studies used LEC and MEC to indicate lateral and medial strips of EC. In the remainder of the manuscript they should only use LEC and MEC when is clear that these are defined, not just indicating a position. This would lead and support their statement in the second paragraph of the Discussion: ‘However, the anatomical organisation and connectivity patterns are largely conserved across species and the characteristic projections from the PRC and PHC rather map on the anteroposterior axis in primates.’

We thank the referees for raising this important issue and pointing out the need for clarification. Indeed, ‘MEC’ and ‘LEC’ could be considered a somewhat unfortunate nomenclature, because a literal interpretation misrepresents the actual anatomical localization of the cytoarchitectonic regions. In response to the reviewers' comment we have made the distinction between cytoarchitectonic regions and anatomical location explicit and altered the manuscript accordingly to avoid confusion.

Partly in response to Comment 5B (see below), we decided to change the naming of the two regions we have identified from ‘pmEC’ and ‘alEC’ to septal EC (sEC) and temporal EC (tEC). We feel that this is a less ambiguous nomenclature for the two regions. Firstly, the mediolateral distinction is negligible, while the septotemporal division is dominant (see new Figure 2–figure supplement 1, below). Secondly, the septotemporal axis can be identified independently of the orientation of a given MRI acquisition and therefore is preferable to ‘anteroposterior axis’.

We therefore modified text as follows:

Introduction: “In rodents—and on the basis of cytoarchitectonics—the EC is typically (Van Strien et al., 2009; Canto et al., 2008, and Kerr et al., 2007) 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 et al., 2011, Knierim et al., 2014, and Ranganath et al., 2012).

Also in the Introduction: “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. […] In contrast, neuroimaging studies on memory in healthy participants (Schultz 1012, Reagh 2014) and participants with preclinical AD (Khan 2013) suggest that the rodent MEC and LEC map on medial and lateral strips of EC in humans.”

Discussion: “While the shape and location of the EC differs between rodents and primates (Witter et al., 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.”

Still in the Discussion section: “Previous neuroimaging studies in humans reported differences between medial and lateral aspects of entorhinal cortex […] Likewise, anterior slices through the temporal EC contain mostly the homologue of the rodent LEC.”

2) The paper can be improved by adding a few simple analyses that can hopefully illustrate the results in a clear manner. Below are potential technical issues that the authors should address. These issues, described in more detail below, should be straightforward to deal with in the revision:

A) The study elegantly demonstrates that pmEC and alEC show different patterns of whole-brain connectivity with extended posterior medial (PM) and anterior temporal (AT) neocortical networks. If we understand the methods, the authors used a single ROI for the entire PM network and one for the entire AT network. At least for illustrative purposes, it would be useful to present separate results for each sphere and each EC ROI. Ideally they could show a correlation matrix with the 4 EC ROIs (well, actually 8, as described in my next point) in columns and the individual PM and then AT ROI spheres in rows. Hopefully, on a quick scan of the matrix, readers will be able to see differences in the connectivity profiles of the anterior lateral and posterior medial EC ROIs.

We thank the reviewers for this suggestion. We think that it is indeed highly relevant to characterise the connectivity profiles of the suggested human homologue of the rodent MEC and LEC. Initially, we showed differential whole-volume connectivity maps (Figure 2D), to meet this goal. However, comment 2A mainly refers to the model-based analysis that required manual segmentation of the EC. We indeed performed two separate analyses. Each time the EC was divided in two halves: Once along the anteroposterior (septotemporal) axis, once along the mediolateral/proximodistal axis. This approach was pursued to ensure comparable number of voxels per parcel and therefore comparable signal-to-noise ratio (SNR) properties per parcel. We have now described the analysis in more detail in the revised text (Results): we created spherical regions-of-interest (ROIs) with 4 mm radius around coordinates pertaining to either of the networks (Ranganath et al., 2012, and Libby et al., 2012) (see Supplementary file 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.

Analysing connectivity of 8 manually segmented EC subregions with single spheres on peak coordinates of the two neocortical networks (AT & PM system, reported by Libby et al. (2012) is an interesting suggestion but also comes with challenges, both on a biological and analytical level: This approach relies on piece-wise constant connectivity patterns, where both the EC subregions and the neocortical regions have an all-or-none connectivity preference. However, the connectivity profile—as determined by invasive tracing studies—of the rodent MEC and LEC is not fully segregated, but rather shows overlap. For example, all EC regions have reciprocal projections to the frontal lobes (Kerr et al., 2007) and our own data (Figure 2D) suggest this is the case in humans as well. Libby et al. (2012) used a Gaussian smoothing kernel of 5 mm FWHM for their functional connectivity analyses (in contrast to 2.5 mm FWHM in our study), which might smooth-out some of the fine-grained differences. The power of the model-based approach results from combining time-courses of a large number of voxels to increase effective SNR. The analysis of connectivity between time-courses of single spheres with 8 EC subregions would suffer from significantly decreased effective SNR.

Nevertheless, we performed the suggested analysis (Author response image 1). Note that our initial selection of the ∼100 coordinates reported in Table 1 of Libby et al. (2012) comprised many unilateral regions. To avoid proximity biases on connectivity with left or right EC regions, we created a new sub-selection of AT & PM regions with bilateral coordinates (4 bilateral spheres per network, i.e. 16 ROIs) and computed connectivity with 8 EC ROIs (4 quadrants in each hemisphere, as suggested in the following point). Author response image 1 shows the resulting connectivity matrix. Note that the division lines of the manually created EC quadrants are roughly parallel to the cardinal axes. This is in contrast to the curved and rather diagonal division line identified with the ConGrads method. Therefore, the anterior-medial and posterior-lateral quadrant likely encompass about equal parts of tEC and sEC. Black boxes in Author response image 1 outline connectivity of the anterior-lateral and posterior-medial EC quadrants to the AT and PM network. Only these fields can be expected to have a unique, albeit fractional, contribution of the tEC and sEC regions defined in the main study with the data-driven ConGrads approach. Restricting the analysis to these quadrants indeed revealed an interaction between EC subregion and cortical network (repeated-measures ANOVA: network * EC region: F(1,21)=6.9 p=0.0155 ). However, this is not readily apparent from Figure R1 itself. Based on the arguments outlined above, we would prefer to not include this analysis in the manuscript.

Author response image 1
Detailed model-based connectivity analyses. Single spheres to EC quadrants across hemispheres.

Functional connectivity (partial correlation coefficients) of spherical ROIs of 4 mm radius, centred on a bilateral selection of coordinates reported by Libby et al. (2012) to 4 EC subregions in both hemispheres. The non-overlapping EC subregions were segmented manually. X-axis labels: the first letter indicates laterality (l vs r), the second anterioposterior location (a vs p), the third mediolateral location (m vs l). Y-axis labels: AT1 = Anterior inferior temporal gyrus; AT2 = Perirhinal cortex; AT3 = Dorsolateral prefrontal cortex; AT4 = Orbitofrontal cortex; PM1 = Medial posterior occipital cortex; PM2 = Retrosplenial cortex; PM3 = Occipital pole; PM4 = parahippocampal cortex. Last letter indicates laterality (l vs r).

https://doi.org/10.7554/eLife.06738.018

B) I did not see any mention of laterality. I might have missed it, but if the authors collapsed across hemispheres, I'd like them to consider also presenting results with separate left and right hemisphere ROIs. Doing so would provide a natural internal replication, because one would expect more extensive contralateral connectivity within the alEC regions and within the pmEC regions than between alEC and pmEC.

We are grateful for the referees' suggestion to test interhemispheric connectivity of the tEC and sEC. As predicted, the tEC is more connected to itself across hemispheres, than to the sEC on either side. This suggests that even though the two different subregions within a hemisphere are in close spatial proximity, they connect much stronger to their homologue region on the contralateral side. We feel that this is an important addition to the article and included these new results in the revised manuscript (new Figure 2–figure supplement 4).

C) Along the same lines as the first 2 points, it appears that the first set of a priori ROI analyses separately examine medial and lateral EC ROIs and anterior and posterior EC ROIs. If the authors planned to investigate both axes, wouldn't it make sense to include all 4 ROIs in a single factorial analysis with anterior-posterior and medial-lateral factors? If the analysis revealed an interaction, it would strengthen the case that the functional organization of the EC cuts across both axes.

We thank the reviewers for this suggestion. We used the connectivity matrix created in response to comment 2A (Author response image 1) for a full factorial analysis including factors for each axis (mediolateral and anteroposterior). This analysis did not reveal interactions between cortical network and the two axes (repeated-measures ANOVA; network* anteroposterior: F(1,21)=1.3, p=0.259; network* mediolateral: F(1,21)=1.77, p=0.198; network* anteroposterior * mediolateral: F(1,21)=3.4, p=0.079). Based on their anatomical location in rodents, the anterior-lateral and posterior-medial quadrants are most likely to distinguish the LEC and the MEC, respectively. Therefore they should exhibit the strongest difference in connectivity between the AT and the PM network. Focusing the analysis to these quadrants indeed revealed an interaction between EC subregion and cortical network (repeated-measures ANOVA: network * EC region: F(1,21)=6.9 p=0.0155 ). Post-hoc t-tests revealed that the anterior-lateral quadrant connected stronger to the AT compared to the PM network (T(21)=4.4, p= 0.0003), while the posterior-medial quadrant did not show this pattern (T(21)=1.5: p=0.15). As outlined above, we feel that this information is not essential to the manuscript and would prefer to not include it in the revised manuscript.

3) It might also help to present whole-brain maps (preferably on a surface rendering) for the contrast between anterior-lateral and posterior-medial ROI connectivity, so that readers can see which regions show differential connectivity. Alternatively, it might be preferable to show single-region connectivity maps.

We thank the reviewers for this suggestion. We agree that whole brain connectivity maps are valuable for illustrating the connectivity profiles of the tEC and sEC. We show whole-volume connectivity maps in Figure 2D and Figure 3D. Crucially, these show strong resemblance of the whole brain connectivity maps of PRC and PHC seeds shown by Libby et al. (2012) and therefore complement the initial model-based analysis.

4A) In general, motion artifact is always a concern with functional connectivity analyses (summarized in Power et al., 2015), and more so when one considers that the EC is typically a site with significant signal dropout. It is surprising that these issues are not raised in the paper. We would like to know whether the pmEC and alEC (either defined arbitrarily or using the ConGrads approach) have different spatial or temporal SNR values.

Following the advice by the referees, we performed additional analyses on spatial and temporal SNR and described the results in Figure 2–figure supplement 3, and in the Results section:

“…we examined both spatial and temporal signal-to-noise ratio (SNR) of the tEC and the sEC (Figure 2–figure supplement 3). Temporal SNR did not differ between tEC and sEC (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 sEC compared to the tEC (mean signal: tEC=4.97; mean signal sEC=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 Methods), which makes it unlikely that signal intensity differences affected the connectivity results.”

Furthermore, the ConGrads method converted the voxel-wise signal to percent-signal-change prior to the functional connectivity analyses. Rather than being sensitive to the magnitude of the signal at a given point in time, our analyses rely on the signal variance over time. Therefore we feel that our results cannot be explained by differences in signal magnitude.

B) Also, the authors used ICA to deal with residual motion artifact, but it is not clear that ICA will adequately remove the effects of transient motion spikes while preserving the rest of the timeseries. It would be reassuring if the authors can replicate the results of at least one of the functional connectivity analyses by using motion scrubbing/censoring instead of ICA correction (see Power et al., 2015).

We thank the reviewers for pointing out the importance of correcting for motion artefacts. Recently it has been shown that ICA-based methods for correcting motion artefacts (such as the FIX approach employed by us) outperform motion scrubbing and spike-regression methods (Pruim et al., 2015, ICA-AROMA: A robust ICA-based strategy for removing motion artefact from fMRI data; Pruim et al., 2015, Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting-state fMRI).

We revised our manuscript accordingly and added the following sentence to the Methods section (in the subsection headed “Data pre-processing”):

“Correction for residual motion artefacts was performed with an ICA-based method, which has been shown to outperform motion scrubbing and spike-regression methods both in terms of reproducibility of resting-state networks and conservation of temporal degrees of freedom”.

We also followed the suggestion of the reviewers and applied motion scrubbing in a separate analysis. The ConGrads analysis yields practically the same results with and without scrubbing. In the initial submission, we already excluded 4 out of 26 participants, based on large numbers of framewise displacements (FD) above 0.5 mm. Now, we extended this approach according to the following description in the Methods section:

“Motion scrubbing. First, we determined time points where instantaneous movement (Power et al., 2012, and Power et al., 2015) exceeded a threshold of 0.5 mm. Then we excluded these time points (volumes) including the one preceding and the two thereafter from subsequent analyses. Hence, per instantaneous movement above threshold, four volumes were removed. On average this resulted in 121 volumes being removed from participant's time-series (range: 4-300).”

In addition, we now describe the outcome in the Results section in the following way:

“We used an ICA-based method for data cleaning (see 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, and Power et al., 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).”

5A) The ConGrads analysis is not clear. Giving the authors the benefit of the doubt (more so if they can present the simple analyses suggested above), but a better and simpler explanation of this method is needed. It sounds like a promising approach, but given that the method is not in widespread use, readers may be put off by the brief explanation.

We expanded and simplified the description of the ConGrads analysis (now mostly referred to as data-driven connectivity analysis) in the following way. The Results section now reads as follows:

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 Methods). […] 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).

In addition, we adapted the Methods section in the following way (subsection headed “Data-driven connectivity analysis”):

“To overcome limitations of the seed-based connectivity analysis, we employed ConGrads (Haak et al., 2014), which allows for tracing the dominant modes of functional connectivity change within a pre-specified region of the brain in a fully data-driven manner. […] For the ROI analyses on the data of the spatial and non-spatial stimulation experiment, the clusters were warped into MNI space.

B) As a related issue, the meaning of the two “connectopies” was difficult to figure out. It sounds like what the authors are saying is that there are two connectivity gradients, one anterior-posterior, and one that is lateral-medial. If so, it would be nice for the authors to spell this out, and to simplify the corresponding caption text for Figure 2 and Figure 3. Also, if the algorithm is pulling out two connectivity gradients, could the relationship be more simply and intuitively captured by a single al-to-pm gradient. If it does not distort the actual patterns in the data, the authors should consider somehow merging the two connectopies to create simple alEC and pmEC ROIs. At present, it is difficult for the reader to look at the figures and see a figure that convincingly depicts the functional topography that is described in the text.

We thank the reviewers for pointing this out. We replaced the term ‘connectopy’ with ‘dominant modes of functional connectivity change’.

Unfortunately, we cannot simply merge the two modes of functional connectivity change. As described above, these represent vectors of multiple, spatially overlapping maps that 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 forth. This is exemplified in Figure 4.

In order to improve the written description of the functional topography and the correspondence with the Figures, we created Figure 2–figure supplement 1 and Figure 3–figure supplement 1 that shows coronal slices of the clusters from the dominant mode of connectivity change. In our view, this figure shows that ‘septal EC’ and ‘temporal EC’ is a more accurate naming than ‘pmEC’ and ‘alEC’.

In the Results section, we modified the text as follows:

“Furthermore, in order to identify the potential human homologues of the rodent LEC and MEC, […] but showed an approximately orthogonal orientation relative to the first (Figure 3 and Figure 3–figure supplement 1).”

Furthermore, we adapted the figure captions (Figure 2 and Figure 3), as requested.

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

In the revised manuscript, the authors have carefully addressed the comments about potential confusion in nomenclature when comparing the human brain to the non-human primate and rodent brain, in particular with respect to the subdivisions of EC. The manuscript has improved significantly, however there are still two remaining issues.

First, the authors suggested to change their initially chosen nomenclature from anterolateral to posteromedial EC into temporal to septal EC. Though understandable, the choice is very counterintuitive, since the septotemporal nomenclature is typically used for the non-primate hippocampus where one tip of the hippocampus is topologically associated with the septal complex. This is not the case in the primate, and therefore, most authors elected to use an anterior-posterior nomenclature. I strongly urge the authors to maintain this convention, irrespective of their valid argument that the AP axis does not completely reflect the tilted orientation of the hippocampal and parahippocampal regions in the human. From what is said in the Results, it is clear that the authors seem ambivalent about their newly introduced nomenclature.

In addition, in view of the fact that this study nicely complements another study on subdivisions of the human EC by Maass et al., this provides a unique and important opportunity to establish a consistent human terminology for the entorhinal cortex. To really imprint a new terminology, this is the likely one and only chance to do so; it would be great if the two papers concur on a common terminology. Knowing the two author groups are in touch, the implementation of a common terminology should be feasible and highly welcome.

We agree that a common nomenclature with the companion article by Maass et al. is highly desirable. As suggested by reviewer #3, we consulted with Maass and colleagues to find a common naming for the entorhinal subregions that we identified. We agreed that the anterior-posterior division is dominant, but by no means the only characterising feature. Both of our studies also find evidence for a medial-lateral distinction. To avoid oversimplification, we agreed to refer to the regions as anterior-lateral and posterior-medial EC (alEC and pmEC, respectively) and adapted the manuscript accordingly.

For clarification, we added this sentence to the Results section: “In addition to the dominant anterior-posterior distinction, the posterior cluster was located more medially (and to some extend more dorsally) and the anterior cluster was located more laterally (and to some extend 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, submitted for publication).”

Furthermore, we corrected the use of ‘LEC’ and ‘MEC’ by replacing it with ‘lateral parts of EC’ and ‘medial parts of EC’.

https://doi.org/10.7554/eLife.06738.017

Article and author information

Author details

  1. Tobias Navarro Schröder

    1. 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
    1. t.navarroschroeder@donders.ru.nl
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0001-6498-1846
  2. Koen V Haak

    1. 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

    1. 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

    1. 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
    1. christian.doeller@donders.ru.nl
    Competing interests
    The authors declare that no competing interests exist.

Funding

European Research Council (ERC) (ERC-StG 261177)

  • Tobias Navarro Schröder
  • Christian F Doeller

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO-Vidi 452-12-009)

  • Tobias Navarro Schröder
  • Christian F Doeller

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO-Vidi 864-12-003)

  • Christian F Beckmann

Wellcome Trust (UK Strategic Award 098369/Z/12/Z)

  • Koen V Haak
  • Christian F Beckmann

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

Acknowledgements

This work was supported by fellowships from the European Research Council (ERC-StG 261177) and the Netherlands Organisation for Scientific Research (NWO-Vidi 452-12-009) awarded to CFD. CFB is supported by the Netherlands Organisation for Scientific Research (NWO-Vidi 864-12-003) and CFB and KVH gratefully acknowledge funding from the Wellcome Trust UK Strategic Award (098369/Z/12/Z). The authors would like to thank A. Vicente-Grabovetsky for help with data analyses, L. Schürmann for providing data, A. Backus for help with data analysis and creation of figures and J. Bellmund, S. Collin and B. Milivojevic for useful discussions and comments on earlier versions of the manuscript.

Ethics

Human subjects: All studies were approved by the local Research Ethics Committees (CMO University Duisburg-Essen, Germany and CMO region Arnhem-Nijmegen, NL and Institutional Review Board ‘IRB # 201204036’; Title: ‘Mapping the HumanConnectome: Structure, Function, and Heritability’, US). Written informed consent was obtained from each participant for data analysis and publication of the study results.

Reviewing Editor

  1. Howard Eichenbaum, Reviewing Editor, Boston University, United States

Publication history

  1. Received: January 29, 2015
  2. Accepted: May 13, 2015
  3. Version of Record published: June 8, 2015 (version 1)

Copyright

© 2015, Navarro Schröder et al

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

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