The many dimensions of human hippocampal organization and (dys)function
Authors: Sarah Genon, Boris C. Bernhardt, Renaud La Joie, Katrin Amunts, Simon B. Eickhoff
Journal: Trends in Neurosciences (2021)

Magnetic resonance imaging–based parcellation of the hippocampal formation and gradient mapping are data-driven techniques that can capture many dimensions of hippocampal organization and provide readily usable outcomes.Features of cortical architecture, such as local connectivity and microstructure, reveal differentiation within the hippocampal formation along the medial–lateral axis. This organizational dimension seemingly reflects local information-processing organization.Neuroimaging markers tapping into hippocampal integration into large-scale networks (i.e., whole-brain connectivity) highlight the long-axis differentiation.The long-axis organization corresponds to a molecular gradient and differential integration across distinct behavioral systems.Capitalizing on gradients and parcellations maps, the long-axis organization of the hippocampal formation can be related to behavioral phenotypes in healthy and clinical populations. The internal organization of hippocampal formation has been studied for more than a century. Although early accounts emphasized its subfields along the medial–lateral axis, findings in recent decades have highlighted also the anterior-to-posterior (i.e., longitudinal) axis as a key contributor to this brain region's functional organization. Hence, understanding of hippocampal function likely demands characterizing both medial-to-lateral and anterior-to-posterior axes, an approach that has been concretized by recent advances in in vivo parcellation and gradient mapping techniques. Following a short historical overview, we review the evidence provided by these approaches in brain-mapping studies, as well as the perspectives they open for addressing the behavioral relevance of the interacting organizational axes in healthy and clinical populations. The internal organization of hippocampal formation has been studied for more than a century. Although early accounts emphasized its subfields along the medial–lateral axis, findings in recent decades have highlighted also the anterior-to-posterior (i.e., longitudinal) axis as a key contributor to this brain region's functional organization. Hence, understanding of hippocampal function likely demands characterizing both medial-to-lateral and anterior-to-posterior axes, an approach that has been concretized by recent advances in in vivo parcellation and gradient mapping techniques. Following a short historical overview, we review the evidence provided by these approaches in brain-mapping studies, as well as the perspectives they open for addressing the behavioral relevance of the interacting organizational axes in healthy and clinical populations. The hippocampal formation is a core brain region from a phylogeny standpoint (cf. the dual origin hypothesis of brain evolution [1.Pandya D. et al.Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept. Oxford University Press, 2015Crossref Google Scholar]), as well as a key node of the human memory system, and is also a target of research in brain disorders such as Alzheimer's disease (AD) and temporal lobe epilepsy. The hippocampal formation is of interest to various fields of research, and numerous models and theories have been proposed regarding its organization, function, and relationship to behavior in the healthy and diseased brain. The traditional anatomical model subdivides the hippocampal formation into discrete subfields arranged along its mediolateral and ventrodorsal axes, including the cornu ammonis (CA1–4) subfields, the dentate gyrus, and the subiculum. The implementation of this model in human research, in particular in neuroimaging studies, is supported by the availability of cytoarchitecture maps (e.g., [2.Amunts K. et al.Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps.Anat. Embryol. 2005; 210: 343-352Crossref PubMed Scopus (835) Google Scholar, 3.Eickhoff S.B. et al.A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data.Neuroimage. 2005; 25: 1325-1335Crossref PubMed Scopus (3051) Google Scholar, 4.Palomero-Gallagher N. et al.Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus.Brain Struct. Funct. 2020; 225: 881-907Crossref PubMed Scopus (13) Google Scholar]), extensive guidelines for manual segmentation (e.g., [5.Wisse L.E. et al.A harmonized segmentation protocol for hippocampal and parahippocampal subregions: why do we need one and what are the key goals?.Hippocampus. 2017; 27: 3-11Crossref PubMed Scopus (74) Google Scholar]), and automated in vivo segmentation tools (e.g., [6.Iglesias J.E. et al.A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI.Neuroimage. 2015; 115: 117-137Crossref PubMed Scopus (551) Google Scholar,7.DeKraker J. et al.Surface-based hippocampal subfield segmentation.Trends Neurosci. 2021; 44: 856-863Abstract Full Text Full Text PDF Scopus (2) Google Scholar]). Through these easily available anatomical maps and segmentation tools, the issue of subfield distinction has achieved a prominent role in the study of behavioral functions and pathophysiological processes. More recently, the organization of the hippocampus along its longitudinal axis has gained considerable interest. Several hypotheses on this organizational pattern have been proposed, such as a tripartite model, differentiating a head from body and tail, and the distinction between the anterior and posterior parts [8.Duvernoy H.M. et al.The Human Hippocampus: An Atlas of Applied Anatomy.4th edn. Springer, 2013Crossref Scopus (112) Google Scholar]. On the basis of anatomical, genetic, and functional data, the relevance of this long-axis organization for behavior has been subject to vivid discussion. It appeared that the literature on human long-axis neuroanatomy was initially less developed than the analogous animal literature [9.Poppenk J. et al.Long-axis specialization of the human hippocampus.Trends Cogn. Sci. 2013; 17: 230-240Abstract Full Text Full Text PDF PubMed Scopus (586) Google Scholar]. Addressing this point, we review how advances in neuroimaging markers of functional and anatomical connectivity, together with the development of parcellation and gradient-mapping approaches, have recently provided substantial empirical support for a differentiation of the hippocampal formation along the long axis [10.Adnan A. et al.Distinct hippocampal functional networks revealed by tractography-based parcellation.Brain Struct. 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Neurosci. 2014; 15: 655-669Crossref PubMed Scopus (814) Google Scholar] that, in humans, discrete changes in molecular or anatomical organization along the hippocampal long axis have yet to be examined. Accordingly, we also review how recent resources, such as BigBrain [21.Amunts K. et al.BigBrain: an ultrahigh-resolution 3D human brain model.Science. 2013; 340: 1472-1475Crossref PubMed Scopus (371) Google Scholar, 22.Amunts K. et al.Julich-Brain: a 3D probabilistic atlas of the human brain's cytoarchitecture.Science. 2020; 369: 988-992Crossref PubMed Scopus (78) Google Scholar, 23.Paquola C. et al.Convergence of cortical types and functional motifs in the human mesiotemporal lobe.eLife. 2020; 9e60673Crossref PubMed Scopus (13) Google Scholar, 24.DeKraker J. et al.Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain.Neuroimage. 2020; 206116328Crossref PubMed Scopus (13) Google Scholar] and genetic mapping, have driven progress in this research [25.Vogel J.W. et al.A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems.Nat. Commun. 2020; 11: 960Crossref PubMed Scopus (36) Google Scholar,26.Ayhan F. et al.Resolving cellular and molecular diversity along the hippocampal anterior-to-posterior axis in humans.Neuron. 2021; 109: 2091-2105.e6Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar]. Considering the interacting nature of these organizational dimensions, we show how they can be synergistically probed by both discrete (with subfield-to-subfield and head–body–tail differentiation) and dimensional approaches (with gradients running in mediolateral and long-axis directions). We also illustrate how such synergy may lead to a unified model of hippocampal organization, function, and pathology. Ex vivo investigations in both animal models and humans, as well as in vivo experiments in animal models, have led to the distinction of several hippocampal subfields. Despite many subfields and their finer subdivisions still being debated (Box 1), it seems generally acknowledged that the hippocampal formation (consisting of the hippocampus itself and the adjacent subiculum) can be subdivided into at least three parts: the CA fields comprising three or even four subfields (CA1–CA4), the dentate gyrus, and the subiculum (or subicular complex) (see Figure I in Box 1), [2.Amunts K. et al.Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps.Anat. Embryol. 2005; 210: 343-352Crossref PubMed Scopus (835) Google Scholar,4.Palomero-Gallagher N. et al.Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus.Brain Struct. Funct. 2020; 225: 881-907Crossref PubMed Scopus (13) Google Scholar,27.Ding S.L. Van Hoesen G.W. Organization and detailed parcellation of human hippocampal head and body regions based on a combined analysis of cyto-and chemoarchitecture.J. Comp. Neurol. 2015; 523: 2233-2253Crossref PubMed Scopus (65) Google Scholar]. Evidence for this organizational model is provided by differences in cytoarchitectonic profiles (see Box 1), as well as physiological findings that reveal different intrinsic connections of these subfields (i.e., within the hippocampal formation) or local ones (within the medial temporal lobe), such as the trisynaptic circuit [28.Andersen P. et al.Lamellar organization of hippocampal excitatory pathways.Exp. Brain Res. 1971; 13: 222-238Crossref PubMed Scopus (656) Google Scholar]. This circuit encompasses the performant path system connecting the entorhinal cortex with the hippocampus, mossy fibers, and Schaffer collaterals. Recent studies have shown that all these pathways consist of multiple components: superficial sheets of fibers emanating from the entorhinal cortex project to the presubiculum and parasubiculum; intermixed transverse and long-axis angular bundle fibers perforate the subiculum and then project to the CA fields and dentate molecular layer; and a significant alvear component runs from the angular bundle to the CA fields [29.Zeineh M.M. et al.Direct visualization and mapping of the spatial course of fiber tracts at microscopic resolution in the human hippocampus.Cereb. Cortex. 2017; 27: 1779-1794PubMed Google Scholar]. These pathways are relevant not only in terms of information processing, but also for understanding disease progression. For instance, it is assumed that the perforant path system is often the first white matter pathway to degenerate in AD [30.Gómez-Isla T. et al.Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer's disease.J. Neurosci. 1996; 16: 4491-4500Crossref PubMed Google Scholar].Box 1Hippocampal subfieldsThe hippocampal formation can be divided into at least three different anatomical parts: the CA fields, the subiculum, and the dentate gyrus. Beyond this broad distinction, finer subdivisions into specific subfields are likely possible but remain debated. Within the CA subregion, Lorente de Nó [35.Lorente de Nó R. Studies on the structure of the cerebral cortex. II. Continuation of the study of the ammonic system.Monatsschr. Psychiatr. Neurol. 1934; 46: 113-177Google Scholar] identified four regions (i.e., CA1, CA2, CA3, and CA4). However, CA2 and CA4 are both not universally accepted as distinct subfields (e.g., [36.Vogt C. Vogt O. Allgemeine Ergebnisse unserer Hirnforschung.1919Google Scholar, 37.Rose J. Zur normalen und pathologischen Architektonik der Ammonsformation.Monatsschr. Psychiatr. Neurol. 1938; 49: 137-186Google Scholar, 38.Amaral D. Insausti R. Hippocampal formation.in: Paxinos G. The Human Nervous System. Academic Press, 1990: 711-755Crossref Google Scholar]). One practical consequence of such scientific disagreements is that although a probabilistic map of CA2 can be found, for example, in the SPM Anatomy Toolbox, a corresponding segment is not available in the FreeSurfer automated segmentation of the hippocampus (Figure IB). These theoretical and practical discrepancies evidently complicate comparison and integration across studies. In the context of segmentation, different protocols may also have different reliability [39.Yushkevich P.A. et al.Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol.Neuroimage. 2015; 111: 526-541Crossref PubMed Scopus (186) Google Scholar,40.Kulaga-Yoskovitz J. et al.Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset.Sci. Data. 2015; 2150059Crossref PubMed Scopus (40) Google Scholar]. Furthermore, finer subdivisions within specific CA subfields, such as subfields within CA1 or the subiculum, have been proposed (see, e.g., [4.Palomero-Gallagher N. et al.Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus.Brain Struct. Funct. 2020; 225: 881-907Crossref PubMed Scopus (13) Google Scholar]) but are still a matter of debate. Acknowledging these diverging viewpoints, one of the most recent mappings of hippocampal subfields can be found in [4.Palomero-Gallagher N. et al.Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus.Brain Struct. Funct. 2020; 225: 881-907Crossref PubMed Scopus (13) Google Scholar]. Resulting from a combination of cytoarchitectonic and receptor architectonic features, the proposed map includes nine distinct regions: fascia dentata (a subregion of the dentate gyrus), CA1, CA2, CA3, CA4, prosubiculum, subiculum proper, presubiculum, and parasubiculum (Figure IA). All in all, microstructural investigations within the hippocampal formation suggest that one can differentiate distinct subfields, whose number diverges across studies, but that are typically aligned along the mediolateral and ventrodorsal axes in humans. The hippocampal formation can be divided into at least three different anatomical parts: the CA fields, the subiculum, and the dentate gyrus. Beyond this broad distinction, finer subdivisions into specific subfields are likely possible but remain debated. Within the CA subregion, Lorente de Nó [35.Lorente de Nó R. Studies on the structure of the cerebral cortex. II. Continuation of the study of the ammonic system.Monatsschr. Psychiatr. Neurol. 1934; 46: 113-177Google Scholar] identified four regions (i.e., CA1, CA2, CA3, and CA4). However, CA2 and CA4 are both not universally accepted as distinct subfields (e.g., [36.Vogt C. Vogt O. Allgemeine Ergebnisse unserer Hirnforschung.1919Google Scholar, 37.Rose J. Zur normalen und pathologischen Architektonik der Ammonsformation.Monatsschr. Psychiatr. Neurol. 1938; 49: 137-186Google Scholar, 38.Amaral D. Insausti R. Hippocampal formation.in: Paxinos G. The Human Nervous System. Academic Press, 1990: 711-755Crossref Google Scholar]). One practical consequence of such scientific disagreements is that although a probabilistic map of CA2 can be found, for example, in the SPM Anatomy Toolbox, a corresponding segment is not available in the FreeSurfer automated segmentation of the hippocampus (Figure IB). These theoretical and practical discrepancies evidently complicate comparison and integration across studies. In the context of segmentation, different protocols may also have different reliability [39.Yushkevich P.A. et al.Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol.Neuroimage. 2015; 111: 526-541Crossref PubMed Scopus (186) Google Scholar,40.Kulaga-Yoskovitz J. et al.Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset.Sci. Data. 2015; 2150059Crossref PubMed Scopus (40) Google Scholar]. Furthermore, finer subdivisions within specific CA subfields, such as subfields within CA1 or the subiculum, have been proposed (see, e.g., [4.Palomero-Gallagher N. et al.Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus.Brain Struct. Funct. 2020; 225: 881-907Crossref PubMed Scopus (13) Google Scholar]) but are still a matter of debate. Acknowledging these diverging viewpoints, one of the most recent mappings of hippocampal subfields can be found in [4.Palomero-Gallagher N. et al.Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus.Brain Struct. Funct. 2020; 225: 881-907Crossref PubMed Scopus (13) Google Scholar]. Resulting from a combination of cytoarchitectonic and receptor architectonic features, the proposed map includes nine distinct regions: fascia dentata (a subregion of the dentate gyrus), CA1, CA2, CA3, CA4, prosubiculum, subiculum proper, presubiculum, and parasubiculum (Figure IA). All in all, microstructural investigations within the hippocampal formation suggest that one can differentiate distinct subfields, whose number diverges across studies, but that are typically aligned along the mediolateral and ventrodorsal axes in humans. The subfield model has hence been well corroborated across techniques and species, using a variety of ex vivo analyses and invasive investigations. The practical implementation of this model for neuroimaging research became crucial in the context of positron emission tomography (PET) and magnetic resonance imaging (MRI) studies. Targeted examinations of imaging characteristics for hippocampal subfields became possible by the availability of cytoarchitectonic maps in stereotaxic Montreal Neurological Institute space [2.Amunts K. et al.Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps.Anat. Embryol. 2005; 210: 343-352Crossref PubMed Scopus (835) Google Scholar,3.Eickhoff S.B. et al.A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data.Neuroimage. 2005; 25: 1325-1335Crossref PubMed Scopus (3051) Google Scholar], the continuous development of guidelines for manual delineation of subfields, in particular by the Hippocampal Subfield Group [5.Wisse L.E. et al.A harmonized segmentation protocol for hippocampal and parahippocampal subregions: why do we need one and what are the key goals?.Hippocampus. 2017; 27: 3-11Crossref PubMed Scopus (74) Google Scholar], and the development of automated segmentation tools (see Box 1 and [31.Dill V. et al.Automated methods for hippocampus segmentation: the evolution and a review of the state of the art.Neuroinformatics. 2015; 13: 133-150Crossref PubMed Scopus (64) Google Scholar] for a review). These resources and developments drove a vast literature of subfield-based studies in the healthy brain, such as with regard to genetic profiles (e.g., [32.van der Meer D. et al.Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes.Mol. Psychiatry. 2020; 25: 3053-3065Crossref PubMed Scopus (34) Google Scholar]), influence of environmental factors (e.g., [33.Marečková K. et al.Perinatal stress and human hippocampal volume: findings from typically developing young adults.Sci. Rep. 2018; 8: 4696Crossref PubMed Scopus (11) Google Scholar]), and aging (e.g., [34.de Flores R. et al.Structural imaging of hippocampal subfields in healthy aging and Alzheimer's disease.Neuroscience. 2015; 309: 29-50Crossref PubMed Scopus (171) Google Scholar]), but also in the diseased brain (cf. below). The most robust support for an anterior–posterior division in the hippocampal formation comes from anatomical and electrophysiological recordings in rodents, showing a differentiation between ventral and dorsal subregions (corresponding to the anterior and posterior human hippocampus, respectively). These include differences in connectivity (for a review, see [20.Strange B.A. et al.Functional organization of the hippocampal longitudinal axis.Nat. Rev. Neurosci. 2014; 15: 655-669Crossref PubMed Scopus (814) Google Scholar]) as well as in functional profiles (for a review, see [9.Poppenk J. et al.Long-axis specialization of the human hippocampus.Trends Cogn. Sci. 2013; 17: 230-240Abstract Full Text Full Text PDF PubMed Scopus (586) Google Scholar]), leading, for instance, to an emotion versus cognition segregation hypothesis [41.Fanselow M.S. Dong H.-W. Are the dorsal and ventral hippocampus functionally distinct structures?.Neuron. 2010; 65: 7-19Abstract Full Text Full Text PDF PubMed Scopus (1993) Google Scholar]. Evidence for an organizational pattern along the long axis is also further supported by gene expression studies in rodents and nonhuman primates (for reviews, see [20.Strange B.A. et al.Functional organization of the hippocampal longitudinal axis.Nat. Rev. Neurosci. 2014; 15: 655-669Crossref PubMed Scopus (814) Google Scholar,42.Cembrowski M.S. et al.Spatial gene-expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons.Neuron. 2016; 89: 351-368Abstract Full Text Full Text PDF PubMed Scopus (160) Google Scholar]). In humans, neuroimaging studies using PET and later functional MRI (fMRI) have led to the 'encoding versus retrieval' hypothesis for the differentiation along the long axis of the hippocampus (the hippocampal encoding/retrieval model [43.Lepage M. et al.Hippocampal PET activations of memory encoding and retrieval: the HIPER model.Hippocampus. 1998; 8: 313-322Crossref PubMed Scopus (432) Google Scholar,44.Tulving E. et al.Novelty and familiarity activations in PET studies of memory encoding and retrieval.Cereb. Cortex. 1996; 6: 71-79Crossref PubMed Google Scholar]). The encoding and retrieval components of this hypothesis were later linked to engagement of the dorsal attention and the default model networks, respectively, leading to the hippocampal encoding/retrieval and network model [45.Kim H. Encoding and retrieval along the long axis of the hippocampus and their relationships with dorsal attention and default mode networks: The HERNET model.Hippocampus. 2015; 25: 500-510Crossref PubMed Scopus (67) Google Scholar]. In this hypothesis-driven framework, MRI was used to compare the connectivity profiles of a priori defined anterior versus posterior subregions. In particular, differences in connectivity profiles along the long axis of the hippocampus were observed when examining functional connectivity based on resting-state fMRI [12.Dalton M.A. et al.Differences in functional connectivity along the anterior-posterior axis of human hippocampal subfields.Neuroimage. 2019; 192: 38-51Crossref PubMed Scopus (37) Google Scholar,46.Qin S. et al.Large-scale intrinsic functional network organization along the long axis of the human medial temporal lobe.Brain Struct. Funct. 2016; 221: 3237-3258Crossref PubMed Scopus (38) Google Scholar], and also when probing anatomical connectivity through diffusion MRI [10.Adnan A. et al.Distinct hippocampal functional networks revealed by tractography-based parcellation.Brain Struct. Funct. 2016; 221: 2999-3012Crossref PubMed Scopus (45) Google Scholar]. Hence, the long-axis organization pattern has gathered increasing support from evidence in rodents and from hypothesis-driven activation and connectivity neuroimaging studies in humans. Nevertheless, to achieve a level of scientific validation comparable to the subfields model, the long-axis organizational pattern should be supported by data-driven investigations in humans and across different neurobiological features tapping into large-scale functional integration and behavioral systems organization. As described in the next section, this became possible by capitalizing on parcellation and gradient approaches. Parcellation and gradient-mapping approaches provide summary representations of spatial variability in a dataset and can hence reveal the topographical patterns of large-scale integration provided by estimates of structural or functional connectivity (Box 2). They thus allow disentangling the differentiation of hippocampus (or subiculum) voxels with regard to their whole-brain connectivity profiles (which represent a multivariate pattern). Parcellation provides a spatial map of parcels (regions) that are internally as homogeneous as possible but at the same time maximally different from each other, whereas gradient mapping will reveal continuous dimensions in a latent space along which the connectivity profiles vary. Below, we first review studies that have applied parcellation approaches to connectivity data of the human hippocampal formation, followed by those that have used gradient methods on similar data. For the sake of comparison and discussion, we also report the few studies that have applied similar, data-driven approaches to markers of local hippocampal structure rather than connectivity patterns.Box 2Parcellation and gradient mapping techniquesIn neuroimaging analyses, 'parcellation' is an umbrella term that covers a variety of approaches to delineate brain regions. For the sake of brevity, here we focus on parcellation using decomposition approaches (i.e., clustering or factorization, in contrast to border detection algorithms; for a review, see [47.Eickhoff S.B. et al.Imaging-based parcellations of the human brain.Nat. Rev. Neurosci. 2018; 19: 672-686Crossref PubMed Scopus (161) Google Scholar]). Gradient mapping techniques, however, often refer to 'dimensional decompositions' (such as principal component analysis or nonlinear variants such as diffusion map embedding [48.Margulies D.S. et al.Situating the default-mode network along a principal gradient of macroscale cortical organization.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 12574-12579Crossref PubMed Scopus (562) Google Scholar,49.de Wael R.V. et al.BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets.Commun. Biol. 2020; 3: 103Crossref PubMed Scopus (58) Google Scholar]). Overall, parcellation and gradient mapping techniques can be applied to the same type of data and simplify spatial variations in a data-driven way. Notably, parcellation approaches focus on discretization into distinct but internally homogeneous regions, whereas gradient mapping identifies continuous axes of feature variation. For example, the pattern of functional connectivity of hippocampal voxels can be summarized by defining groups of voxels that show a similar pattern of connectivity, or, alternatively, it can be summarized as principal gradients along which the connectivity profile of the voxels varies. Generally, the first principal dimensions along which the voxels vary (explaining most variance) will determine the first levels of subdivisions. These approaches are often used in a data-driven framework – that is, without specifying a priori the number of clusters or principal gradients to be extracted from the data. Different metrics can evaluate the quality of fit in addition to assessments of reproducibility and correspondence with other neural features [50.Eickhoff S.B. et al.Connectivity-based parcellation: critique and implications.Hum. Brain Mapp. 2015; 36: 4771-4792Crossref PubMed Scopus (174) Google Scholar]. Additionally, a model selection approach based on statistical testing against a null