Robin De Flores

Renaud La

Gaël Chételat

Structural imaging of hippocampal subfields in healthy aging and Alzheimer's disease

Keywords: Hippocampus, magnetic resonance imaging, aging, Alzheimer's disease, CA1, biomarker Highlights Deformation High-Dimensional Brain Mapping, ASHS: Automatic Segmentation of Hippocampal Subfields, VBM: Voxel Based Morphometry , sub: subiculum, presub: presubiculum, CA: Cornu Ammonis,

Hippocampal atrophy, as evidenced using magnetic resonance imaging (MRI), is one of the most validated, easily accessible and widely used biomarkers of Alzheimer's disease (AD). However, its imperfect sensitivity and specificity have highlighted the need to improve the analysis of MRI data. Based on neuropathological data showing a differential vulnerability of hippocampal subfields to AD processes, neuroimaging researchers have tried to capture corresponding morphological changes within the hippocampus.

The present review provides an overview of the methodological developments that allow the assessment of hippocampal subfield morphology in vivo, and summarizes the results of studies looking at the effects of AD and normal aging on these structures.

Most studies highlighted a focal atrophy of the CA1 subfield in the early (predementia or even preclinical) stages of AD, before atrophy becomes more widespread at the dementia stage, consistent with the pathological literature. Preliminary studies have indicated that looking at this focal atrophy pattern rather than standard whole hippocampus volumetry improves diagnostic accuracy at the Mild Cognitive Impairment (MCI) stage. However, controversies remain regarding changes in hippocampal subfield structure in normal aging and regarding correlations between specific subfield volume and memory abilities, very likely because of the strong methodological variability between studies.

Overall, hippocampal subfield analysis has proven to be a promising technique in the study of AD. However, harmonization of segmentation protocols and studies on larger samples are needed to enable accurate comparisons between studies and to confirm the clinical utility of these techniques.

Introduction

Although the term "hippocampus" was first used in 1587 by Arantius1 , it was only in the second part of the 20 th century that scientists got the opportunity to thoroughly study this small medial temporal lobe structure, which has become one of the most scrutinized regions of the brain. Major advances were first made through the examination of patients with hippocampal lesions, the most famous being Henry Molaison [START_REF] Scoville | Loss of recent memory after bilateral hippocampal lesions[END_REF]; see [START_REF] Squire | The legacy of patient H.M. for neuroscience[END_REF] for review). More recently, the development of brain imaging has assisted researchers in investigating the role of the hippocampus in episodic memory processes in healthy individuals [START_REF] Squire | Activation of the hippocampus in normal humans: a functional anatomical study of memory[END_REF][START_REF] Tulving | Episodic and declarative memory: role of the hippocampus[END_REF]; see [START_REF] Spaniol | Event-related fMRI studies of episodic encoding and retrieval: meta-analyses using activation likelihood estimation[END_REF] for review), as well as in patients with hippocampal damage, providing evidence that this structure is vulnerable to myriad neurological and psychiatric diseases (Geuze et al., 2005a;[START_REF] Small S A | A pathophysiological framework of hippocampal dysfunction in ageing and disease[END_REF]. Because of its important prevalence in the elderly and major socioeconomic impact (Alzheimer's Association, 2014;[START_REF] Diluca | The cost of brain diseases: a burden or a challenge?[END_REF] Alzheimer's disease (AD) is probably the most documented example of these conditions.

For more than two decades, the volume of the whole hippocampus has been assessed through manual delineation of the structure's contours on anatomical images, classically T1weighted isotropic magnetic resonance imaging (MRI) scans. This method has been used in the study of AD since the late 80's [START_REF] Seab | Quantitative NMR measurements of hippocampal atrophy in Alzheimer's disease. Magnetic resonance in medicine[END_REF], and has led to a tremendous number of publications in the field of neuroimaging. More recently, several automatic segmentation methods have been developed (eg. [START_REF] Brewer | Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease[END_REF][START_REF] Chupin | Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation[END_REF][START_REF] Fischl | Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain[END_REF][START_REF] Leung | Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease[END_REF][START_REF] Morra | Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease mild cognitive impairment, and elderly controls[END_REF][START_REF] Patenaude | A Bayesian model of shape and appearance for subcortical brain segmentation[END_REF], enabling less time-consuming and more reproducible segmentations of the structure. From this perspective, major efforts have been made to provide fully automated methods that could be used in clinical routine (Suppa et al., 2015a(Suppa et al., , 2015b)).

While most of the imaging literature has studied the hippocampus as a single unitary entity, it is acknowledged that this structure is heterogeneous and can be divided into subregions with different functions, connectivity to other brain regions and vulnerability to disease [START_REF] Aggleton | Multiple anatomical systems embedded within the primate medial temporal lobe: implications for hippocampal function[END_REF][START_REF] Maruszak | Why looking at the whole hippocampus is not enough-a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer's disease diagnosis[END_REF][START_REF] Small S A | A pathophysiological framework of hippocampal dysfunction in ageing and disease[END_REF]. This heterogeneity is found both along the anterior-posterior axis of the hippocampus and across its different cytoarchitectonic subfields, which include the cornu ammonis fields (CA1-CA42 ), the dentate gyrus (DG), and the subiculum. The idea of distinguishing hippocampal substructures from neuroimaging data is not new; a few authors have measured the volume of hippocampal regions along the anterior-posterior axis, namely the head, body and tail (see [START_REF] Poppenk | Long-axis specialization of the human hippocampus[END_REF] for a discussion on strategies for long-axis segmentation), and have explored differential effects of aging or disease across these substructures [START_REF] Driscoll | The aging hippocampus: cognitive, biochemical and structural findings[END_REF][START_REF] Gordon B A | Effects of aging and Alzheimer's disease along the longitudinal axis of the hippocampus[END_REF][START_REF] Jack | Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease[END_REF][START_REF] Malykhin | Aging hippocampus and amygdala[END_REF][START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF], or assessed anteriorposterior gradient in normal hippocampal function and connectivity [START_REF] Aggleton | Multiple anatomical systems embedded within the primate medial temporal lobe: implications for hippocampal function[END_REF][START_REF] Poppenk | Long-axis specialization of the human hippocampus[END_REF]La Joie et al., 2014a;[START_REF] Chase | Evidence for an anterior-posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: focus on the subiculum[END_REF][START_REF] Barnett | Distinct hippocampal functional networks revealed by tractography-based parcellation[END_REF]. However, it is only more recently, with the development of more sophisticated neuroimaging methods and the emergence of high field MR scanners providing high (i.e. submillimetric) resolution images, that the assessment of the hippocampal subfields has become accessible to human neuroimaging.

In the present review, we offer to give an overview of these methodological advances, summarize the results of structural imaging studies assessing the effects of AD and healthy aging on hippocampal subfields, as well as the potential interest of these techniques, both in terms of diagnostic value and for the understanding of memory deficits.

2. From the hippocampus to hippocampal subfields: 15 years of technical and methodological progresses.

Indirect surface-based approaches using computational neuroanatomy tools

In the early 2000's, new approaches were developed to study the shape of the hippocampus and these methods gained popularity in assessing age or disease-related modifications in hippocampal morphology beyond volumetric changes. These various techniques (eg. large deformation high-dimensional brain mapping [START_REF] Csernansky | Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Dementia of the Alzheimer type[END_REF][START_REF] Wang | Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging[END_REF], radial atrophy (Apostolova et al., 2006a;[START_REF] Frisoni | In vivo neuropathology of the hippocampal formation in AD: a radial mapping MR-based study[END_REF][START_REF] Thompson | Mapping hippocampal and ventricular change in Alzheimer disease[END_REF] or spherical harmonics [START_REF] Gerardin | Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging[END_REF][START_REF] Lindberg | Hippocampal shape analysis in Alzheimer's disease and frontotemporal lobar degeneration subtypes[END_REF][START_REF] Sarazin | The amnestic syndrome of hippocampal type in Alzheimer's disease: an MRI study[END_REF]), are generally based on four main steps: 1) (manual or automated) segmentation of the whole hippocampus, 2) surface reconstruction, i.e. converting segmentations to surface meshes, 3) across subject alignment of the surfaces, 4) between subject comparison of hippocampal surfaces, usually through the computation of displacement vectors indicating the difference between each subject and a reference (either an average surface or a template). Eventually, these methods provide images showing local areas of inward or outward displacement of the surface in relation to age, disease, or cognitive performance. From these measures, volumetric changes in subfields are inferred, depending on the location of these modifications on the hippocampal surface and using a 3-dimentional atlas of hippocampal subfields (for examples, see Figure 1). It should be acknowledged that these sophisticated methods only provide indirect indication of subfield atrophy, and it is not clear how the atrophy of the deepest regions of the hippocampus (ie CA4-DG) would impact the outer surface of the structure.

Our lab developed a variant of these methods; instead of analyzing surfaces directly, we used the Voxel Based Morphometry (VBM) procedure [START_REF] Ashburner | Voxel-based morphometry--the methods[END_REF][START_REF] Ashburner | A fast diffeomorphic image registration algorithm[END_REF] to analyze grey matter volume changes in every voxel of the brain, and projected the subsequent results onto a 3D hippocampal surface [START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF][START_REF] Fouquet | Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild Cognitive Impairment[END_REF][START_REF] Joie | Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence[END_REF]. Other groups [START_REF] Atienza | Associative memory deficits in mild cognitive impairment: the role of hippocampal formation[END_REF][START_REF] Thomann | Hippocampal and entorhinal cortex volume decline in cognitively intact elderly[END_REF][START_REF] Ziegler | Brain structural trajectories over the adult lifespan[END_REF] also used VBM to assess the volume of hippocampal subfields, extracting the volumes of regions of interests derived from probabilistic cytoarchitectonic atlases [START_REF] Amunts | Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps[END_REF].

Subfield volumetry

2.2.1. Volumetry from high-resolution images: multiple segmentation protocols applied on various images.

Manual delineation

Over the past years, with the introduction of higher fields MRI scanners (3T, 4T, 7T or 9.4T), several high-resolution sequences centered on the medial temporal lobe have been developed, for both structural and functional MRI studies. These oblique coronal images, oriented perpendicular to the longitudinal axis of the hippocampus, have enough contrast and in-plane resolution (i.e. submillimetric) to visualize internal details within the hippocampus, therefore providing anatomic landmarks that can be used to distinguish subregions and perform direct volumetric studies. However, it is important to keep in mind that, in spite of this high resolution, it is still not yet possible to directly visualize the border between two contiguous subfields (eg. CA1 and subiculum): high-resolution neuroimaging is not in vivo histology. Subfield boundaries therefore rely on landmarks derived from anatomical atlases and geometric rules that are set to reach a compromise between reliability/reproducibility and validity: in vivo volumetric measurements are only approximations of the actual subfield volumes.

The strong between-study heterogeneity of segmentation protocols should also be highlighted, as multiple differences can be found in terms of i) number of segmented subfields, ii) which subfields are segmented separately or grouped together, iii) subfield borders and, iv) whether subfield segmentation is performed on the full length of the hippocampus or only on the body (see figure 2 for an illustration of this heterogeneity). This variability is not only due to differences in the atlases used as references, but also to the technical characteristics of the MRI data: scanner field strength (usually between 3T and 7T), image weighting (usually T2, but also proton density) and resolution (see the Tables for a global overview of the sequence variability). For example, [START_REF] Wisse | Subfields of the hippocampal formation at 7 T MRI: in vivo volumetric assessment[END_REF] acquired T2weighted images with isotropic voxels (0.7 x 0.7 x 0.7 mm) on a 7T scanner to segment 5 regions (subiculum, CA1, CA2, CA3, CA4-DG) along the full axis of the hippocampus, while we manually delineate 3 regions (subiculum, CA1, CA2-3-4-DG) along the entire hippocampus using proton density weighted images acquired on a 3T scanner with a resolution of 0.375 x 0.375 x 2 mm [START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF][START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] Dubois | Revising the definition of Alzheimer's disease: a new lexicon[END_REF], and [START_REF] Mueller | Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T[END_REF][START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF][START_REF] Mueller | Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease[END_REF] use T2-weighted images with a 0.4 x 0.4 x 2 mm resolution from a 4T scanner to delineate 4 regions (subiculum, CA1, CA2, CA3-DG) in the hippocampal body. For a deeper and more exhaustive analysis of the various elements of between-study variability -which is not the purpose of the current reviewthe reader is referred to a very recent report from a group of hippocampal subfield experts who qualitatively and quantitatively compared 21 segmentation protocols (Yushkevich et al., 2015a).

In addition to volumetric analyses of hippocampal subfields, alternative methods have been proposed to analyze these medial temporal lobe-centered high-resolution images. Notably, some authors have used a cortical unfolding technique to enhance the visibility of the convoluted medial temporal lobe cortex by flattening the entire gray matter volume into twodimensional space [START_REF] Burggren A | Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers[END_REF]Donix et al., 2010aDonix et al., , 2010b;;[START_REF] Ekstrom | Advances in high-resolution imaging and computational unfolding of the human hippocampus[END_REF]. This allows the thickness of the cortical ribbon throughout each identified subregion to be examined separately, and could provide sensitive information that could be complementary, and not redundant with volumetric measures. [START_REF] Kerchner | Hippocampal CA1 apical neuropil atrophy in mild Alzheimer disease visualized with 7-T MRI[END_REF][START_REF] Kerchner G A | Hippocampal CA1 apical neuropil atrophy and memory performance in Alzheimer's disease[END_REF][START_REF] Kerchner | Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: a seven tesla magnetic resonance imaging study[END_REF][START_REF] Kerchner | APOE ε4 worsens hippocampal CA1 apical neuropil atrophy and episodic memory[END_REF] developed another approach that was directly influenced by several neuropathological studies showing that AD-related neurodegeneration is not only subfield-, but also strata-specific, with a stronger vulnerability of the synapse-rich apical neuropil layer (the stratum radiatum and stratum lacunosum-moleculare, SRLM) as compared to the cell body layer (the stratum pyramidale, SP). Using 7T T2-weighted MR images with ultra high resolution (0.22 x 0.22 x 1.5 mm voxels), they measured different metrics in the body of the hippocampus, including the width (or thickness) of CA1-SRLM, defined as the hypointense band between the more intense DG and CA1-SP cell layers.

Toward automated methods

Similar to global hippocampal volumetry (see [START_REF] Dill | Automated Methods for Hippocampus Segmentation: the Evolution and a Review of the State of the Art[END_REF] for a review), a few automatic methods have been developed to facilitate subfield assessment in research and for potential clinical application without requiring as much time, labor and anatomical expertise as manual delineation.

Notably, a group from the University of Pennsylvania initially developed an open-source, semi-automatic method called Automatic Segmentation of Hippocampal Subfields (ASHS), designed to be used on high-resolution anisotropic T2 images [START_REF] Yushkevich | Nearly automatic segmentation of hippocampal subfields in in vivo focal T2weighted MRI[END_REF]. This algorithm initially relied on the segmentation protocol developed by [START_REF] Mueller | Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T[END_REF], so that it was restricted to the hippocampal body, and was used in the context of aging and AD [START_REF] Pluta | In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI[END_REF]. ASHS was recently improved (Yushkevich et al., 2015b) to i) identify more numerous hippocampal subfields and parahippocampal subregions, ii) segment these along the full length of the hippocampus, iii) allow both volume and thickness analysis. This updated, fully automated version of ASHS requires a classical T1-weighted image in addition to the high-resolution T2-weighted MRI.

Subfield volumetry using standard 1mm3 isotropic T1-weighted images

In parallel, methods to measure the volume of hippocampal subfields using standard (≈1mm 3 isotropic) images have also been developed. The most widely used is that implemented in FreeSurfer 5.3, and has been developed and validated using ultra-high resolution in vivo T1-weighted images [START_REF] Van Leemput | Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI[END_REF]. Using this technique, 10 region labels (including subfields per se, but also hippocampal fissure, choroid plexus, fimbria and inferior lateral ventricle) are propagated from an ultra-high resolution template by applying heuristic rules based on the anatomy of the whole hippocampus rather than using internal landmarks within the hippocampus (which are not visible on standard scans). Because this method is user-friendly and directly applicable to standard 1.5T or 3T T1-weighted scans, it has been used extensively over the last several years to study hippocampal changes related to development, aging and various conditions [START_REF] Aas | Interplay between childhood trauma and BDNF val66met variants on blood BDNF mRNA levels and on hippocampus subfields volumes in schizophrenia spectrum and bipolar disorders[END_REF][START_REF] Durazzo | Interactive effects of chronic cigarette smoking and age on hippocampal volumes[END_REF][START_REF] Engvig | Hippocampal subfield volumes correlate with memory training benefit in subjective memory impairment[END_REF][START_REF] Ezzati | Hippocampal subfields differentially correlate with chronic pain in older adults[END_REF][START_REF] Francis | Reduced subicular subdivisions of the hippocampal formation and verbal declarative memory impairments in young relatives at risk for schizophrenia[END_REF][START_REF] Hanseeuw | Mild cognitive impairment: differential atrophy in the hippocampal subfields[END_REF][START_REF] Haukvik | In Vivo Hippocampal Subfield Volumes in Schizophrenia and Bipolar Disorder[END_REF][START_REF] Hsu | Amyloid Burden in Cognitively Normal Elderly is Associated with Preferential Hippocampal Subfield Volume Loss[END_REF][START_REF] Khan | Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts[END_REF][START_REF] Krogsrud | Development of hippocampal subfield volumes from 4 to 22 years[END_REF][START_REF] Kühn | Plasticity of hippocampal subfield volume cornu ammonis 2+3 over the course of withdrawal in patients with alcohol dependence[END_REF]Lim et al., 2012aLim et al., , 2012b;;[START_REF] Pereira | Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI[END_REF][START_REF] Pereira | Regional vulnerability of hippocampal subfields and memory deficits in Parkinson's disease[END_REF][START_REF] Tamnes | Regional hippocampal volumes and development predict learning and memory[END_REF]Teicher et al., 2012 among others).

However, some authors have recently expressed their concerns about the subfield segmentation tool implemented in FreeSurfer 5. 3 (de Flores et al., 2015;[START_REF] Pluta | In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI[END_REF]Wisse et al., 2014a;Yushkevich et al., 2015b), arguing that this method has not been validated on the standard images on which it is commonly used. More precisely, doubts are cast upon the ability to distinguish (numerous) subfields using low-resolution images, but also about the boundaries of the parcellation scheme, which strongly differs from the majority of in vivo and imaging atlases. For instance, FreeSurfer 5.3's CA1 is the smallest subfield while CA2-3 is the biggest, which contrasts with histologic data showing the opposite (for discussion, see [START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF]and Wisse et al., 2014a, Yuschevich et al., 2015). These specificities of the FreeSurfer 5.3 subfield segmentation package must be recognized when interpreting the abundance of results based on this broadly used but controversial method (see below).

Very recently, the creators of FreeSurfer 5.3 subfield segmentation tool have acknowledged the flaws of their initial method, notably explaining that "The delineation protocol of the in vivo atlas was designed for the hippocampal body and did not translate well to the hippocampal head or tail" (see the official website https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfieldSegmentation ; note that the tool is now declared "deprecated"). Instead, they have developed an alternative tool implemented in FreeSurfer 6.0 (Iglesias et al., 2015). Briefly, this new method uses a specifically developed atlas developed from 15 subjects with ex vivo 7T MRI scans and comprised 13 different labels. Importantly, FreeSurfer 6.0 tool can be used using classical (isotropic) T1-weighted scans, specific (anisotropic) T2-seighted images, or both. As a consequence of these major changes, results are likely to be very different from these obtained with FreeSurfer 5.3. Indeed, Iglesias et al (2015) used both FreeSurfer 5.3 and 6.0 on T1-MRI from AD patients and normal controls and found important discrepancies, e.g. CA1 went from one of the least different to one of the most different subfields (see Iglesias et al 2015, table 6 and present review, Table 1). Results obtained with the FreeSurfer 6.0 thus appear more accurate than its predecessor (see below); yet, because of its novelty, it has not been widely used and tested yet.

Another approach has been developed through the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm, which minimises the number of input atlases needed [START_REF] Pipitone | Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates[END_REF] by creating a template library from a sample of the subject images. So far, MAGeT-Brain has been used on normal controls [START_REF] Pipitone | Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates[END_REF] and depressed patients [START_REF] Treadway | Illness progression, recent stress, and morphometry of hippocampal subfields and medial prefrontal cortex in major depression[END_REF] to segment subfields on T1-weighted images using the 5 ultra-high resolution subfield images from the Winterburn atlas [START_REF] Winterburn | A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging[END_REF] healthy controls, age range = 29-57) as inputs. To date, this technique has not been used in aging or dementia, and future studies are needed to assess how the algorithm will handle the segmentation in elder individuals with major atrophy, and whether other (or additional) agematched manually segmented subfield images are needed as inputs.

Alzheimer's disease

AD is the most common type of dementia, accounting for 60-80% of cases (Alzheimer's Association, 2014). Recent epidemiological studies estimated that 44.35 million people are affected by dementia worldwide and projected that the prevalence would reach 75.62 million by 2030 (Alzheimer's Disease International, 2013), mainly because of population aging.

Although dementia is not the most prevalent brain disorder in Europe and other western countries, its global socio-economic impact is tremendous because of the high individual cost it causes (DiLuca and Olesen, 2014).

Pathologically, AD is characterized by two main pathological hallmarks: extracellular amyloid deposits composed of insoluble amyloid beta (Aβ) protein, and intra-neuronal neurofibrillary tangles (NFTs) containing hyperphosphorylated tau protein [START_REF] Hyman | National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF][START_REF] Khachaturian | Diagnosis of Alzheimer's disease[END_REF][START_REF] Markesbery | Neuropathological criteria for the diagnosis of Alzheimer's disease[END_REF][START_REF] Mirra | The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer's disease[END_REF][START_REF] Montine | National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease: a practical approach[END_REF]. Along with these two proteinopathies, AD is characterized by significant loss of neurons and synapses, resulting in brain shrinkage (see Duyckaerts et al., 2009 for review).

From a clinical perspective, AD is classically diagnosed at the dementia stage when cognitive deficits already significantly impact activities of daily living [START_REF] Mckhann | Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease[END_REF]. However, because of the progressive nature of cognitive decline and underlying pathology, it is now fully accepted that the disease is characterized by an asymptomatic or 'preclinical' stage (where pathological features are developing in individuals with normal cognition, as observed either at autopsy or in vivo using biomarkers), followed by a predementia or prodromal stage, where symptoms are detectable but not sufficient to meet criteria for dementia. This later stage usually but not always corresponds to the clinically defined amnestic subtype of mild cognitive impairment (MCI), characterized by the presence of memory complaint together with isolated memory impairment as objectivized by a neuropsychological examination. However, (amnestic) MCI is a heterogeneous entity that can be due to various underlying neurological or psychiatric etiologies [START_REF] Hughes | Should mild cognitive impairment be subtyped?[END_REF]. In a meta-analysis of 41 clinical studies of MCI patients with a minimal longitudinal follow up of 3 years, annual conversion rate to AD was estimated around 7% for MCI and almost 12% for amnestic MCI, while relative risk of AD (as compared to cognitively normal individuals) was around 9-fold [START_REF] Mitchell A | Rate of progression of mild cognitive impairment to dementia--meta-analysis of 41 robust inception cohort studies[END_REF]). Yet, not all (amnestic) MCI patients develop AD or dementia, even after a decade, and a significant proportion of them remain stable or even revert to normal cognition during the follow up period [START_REF] Ganguli | Outcomes of mild cognitive impairment by definition: a population study[END_REF][START_REF] Koepsell | Reversion from mild cognitive impairment to normal or near-normal cognition: risk factors and prognosis[END_REF]. This important variability in MCI outcomes stresses the need to develop methods and tools enabling the identification of the MCI patients who are the most likely to eventually progress to dementia, for both patients' care and potential inclusion in clinical trials.

Hippocampal damage in AD: previous knowledge from neuropathology and global volumetry

The hippocampus is one of the major targets of AD pathological hallmarks, especially of neurofibrillary tangles (NFT) and neuron and neurite loss. In the hippocampus, NFT first target CA1, then the subiculum, CA2, CA3 and CA4/DG [START_REF] Braak | Neuropathological stageing of Alzheimer-related changes[END_REF][START_REF] Braak | Staging of Alzheimer-related cortical destruction[END_REF][START_REF] Fukutani | Neuronal loss and neurofibrillary degeneration in the hippocampal cortex in late-onset sporadic Alzheimer's disease[END_REF][START_REF] Lace | Hippocampal tau pathology is related to neuroanatomical connections: an ageing population-based study[END_REF][START_REF] Schönheit | Spatial and temporal relationships between plaques and tangles in Alzheimer-pathology[END_REF]. As for neuronal loss, a histological study reported major losses in AD patients in CA1 (68%) as well as in the subiculum (47%) and hilus (25%) in comparison with age-matched controls [START_REF] West | Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer's disease[END_REF]. By contrast, no significant neuronal loss was found in cognitively normal elders with substantial neuritic Aβ plaques in their brain [START_REF] West | Hippocampal neurons in pre-clinical Alzheimer's disease[END_REF][START_REF] Price | Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease[END_REF]. Moreover, [START_REF] Price | Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease[END_REF] showed an incremental decrease in CA1 neurons from 29% in very mild AD patients to 38% in AD patients with severe dementia. Overall, neuronal loss has been found to predominate in the CA1 subfield in most studies [START_REF] Kril | Patients with vascular dementia due to microvascular pathology have significant hippocampal neuronal loss[END_REF][START_REF] Padurariu | HIPPOCAMPAL NEURONAL LOSS IN THE CA1 AND CA3 AREAS OF ALZHEIMER[END_REF][START_REF] Von Gunten | Cognitive impact of neuronal pathology in the entorhinal cortex and CA1 field in Alzheimer's disease[END_REF], although discrepant findings have also been reported [START_REF] Simic | Volume and number of neurons of the human hippocampal formation in normal aging and Alzheimer's disease[END_REF]. Moreover, CA1 neuronal loss was found to correlate with the density of NFT [START_REF] Fukutani | Neuronal loss and neurofibrillary degeneration in the hippocampal cortex in late-onset sporadic Alzheimer's disease[END_REF][START_REF] Von Gunten | Cognitive impact of neuronal pathology in the entorhinal cortex and CA1 field in Alzheimer's disease[END_REF].

In line with the pathological literature, neuroimaging studies have shown that the hippocampus is already significantly damaged at the time of AD (dementia) diagnosis:

according to a meta-analysis of structural imaging studies including 700 patients with mild to moderate dementia, the volume loss is around 23-24% as compared to age-matched controls [START_REF] Shi | Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies[END_REF]. Longitudinal studies conducted on cohorts of cognitively normal elders have reported that hippocampal atrophy is already present in the preclinical stage, ie before the stage of MCI [START_REF] Bernard | Time course of brain volume changes in the preclinical phase of Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF][START_REF] Smith | Structural brain alterations before mild cognitive impairment in ADNI: validation of volume loss in a predefined anterotemporal region[END_REF][START_REF] Smith | Brain structural alterations before mild cognitive impairment[END_REF], up to 10 years before the diagnosis of dementia [START_REF] Tondelli | Structural MRI changes detectable up to ten years before clinical Alzheimer's disease[END_REF].

Subfield imaging in AD

Dementia and predementia stages

Table 1 summarizes the main results from studies conducted in patients with AD or MCI.

When comparing AD patients to healthy controls using surface-based techniques, studies have systematically described major inward surface deformation along the lateral zone of the hippocampus, which corresponds to the CA1 subfield (see Figure 1 for examples of subfield display on the surface). This result is consensual as it is independent from the specific method used: it has been reported using radial atrophy [START_REF] Frisoni | Mapping local hippocampal changes in Alzheimer's disease and normal ageing with MRI at 3 Tesla[END_REF][START_REF] Frisoni | In vivo neuropathology of the hippocampal formation in AD: a radial mapping MR-based study[END_REF][START_REF] Scher A | Hippocampal shape analysis in Alzheimer's disease: a population-based study[END_REF], large deformation high-dimensional brain mapping [START_REF] Csernansky | Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Dementia of the Alzheimer type[END_REF][START_REF] Tang | Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting[END_REF][START_REF] Tepest | Hippocampal surface analysis in subjective memory impairment, mild cognitive impairment and Alzheimer's dementia[END_REF][START_REF] Wang | Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type[END_REF][START_REF] Wang | Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging[END_REF], spherical harmonics [START_REF] Gerardin | Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging[END_REF][START_REF] Lindberg | Hippocampal shape analysis in Alzheimer's disease and frontotemporal lobar degeneration subtypes[END_REF] or a VBM-based analysis [START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF]; see Figure 1 for an illustration. In some studies, abnormalities were also found in medial regions of the surface, mostly corresponding to the (pre)subiculum [START_REF] Frisoni | Mapping local hippocampal changes in Alzheimer's disease and normal ageing with MRI at 3 Tesla[END_REF][START_REF] Frisoni | In vivo neuropathology of the hippocampal formation in AD: a radial mapping MR-based study[END_REF][START_REF] Wang | Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type[END_REF] or even in all subfields [START_REF] Apostolova | Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease[END_REF][START_REF] Chow | Comparing hippocampal atrophy in Alzheimer's dementia and dementia with lewy bodies[END_REF]Chow et al., , 2015)). Using direct volumetry in AD patients, several studies reported significant atrophy in all, or almost all, investigated subfields [START_REF] Adachi | Morphology of the Inner Structure of the Hippocampal Formation in Alzheimer Disease[END_REF][START_REF] Boutet | Detection of volume loss in hippocampal layers in Alzheimer's disease using 7 T MRI: a feasibility study[END_REF][START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF][START_REF] Khan | Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts[END_REF][START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] Li | Discriminative analysis of mild Alzheimer's disease and normal aging using volume of hippocampal subfields and hippocampal mean diffusivity: an in vivo magnetic resonance imaging study[END_REF], the major atrophy being generally located in CA1 (La [START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF][START_REF] Mueller | Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease[END_REF]. In a few studies, some subfields were (relatively) spared, namely CA3/DG [START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF]Mueller et al., 2010) or CA2 (Wisse et al., 2014b).

Among MCI patients, shape analysis identified a more focal pattern of atrophy, usually restricted to the lateral side of the hippocampus corresponding to CA1, and sometimes also including the subiculum [START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF][START_REF] Gerardin | Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging[END_REF][START_REF] Tang | Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting[END_REF][START_REF] Tepest | Hippocampal surface analysis in subjective memory impairment, mild cognitive impairment and Alzheimer's dementia[END_REF]. Volumetric studies showed a volume reduction predominant in CA1, and also involving either CA3/DG (Yassa et al., 2010b), CA4/DG [START_REF] Pluta | In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI[END_REF] or the subiculum [START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF][START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF]. In those two latter studies (performed in an overlapping sample), the mean volume loss in MCI patients was estimated at 20% for CA1 and 15% for the subiculum. Note that Mueller et al. specifically assessed the CA1-2 transition area and reported predominant atrophy in this region in MCI patients [START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF][START_REF] Mueller | Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease[END_REF]. Negative findings (i.e. no significant atrophy in any hippocampal subfield) have also been reported, potentially because of low statistical power [START_REF] Kerchner | Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: a seven tesla magnetic resonance imaging study[END_REF]Wisse et al., 2014b).

Interestingly, the relatively specific pattern found in MCI (CA1 alone or CA1 and subiculum) was even more pronounced in the patients that later converted to dementia (Apostolova et al., 2006b;[START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF], while transition from MCI to dementia seems to be associated with a spreading of atrophy to the rest of the hippocampus (Apostolova et al., 2010c;[START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] Mueller | Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease[END_REF].

A couple of studies (La [START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] Ye | Hippocampal and cortical atrophy in amyloid-negative mild cognitive impairments: comparison with amyloid-positive mild cognitive impairment[END_REF] have compared the pattern of hippocampal atrophy in MCI patients with and without Aβ pathology (Aβ+ and Aβ-, respectively), the former group being at higher risk of rapid cognitive decline [START_REF] Doraiswamy | Florbetapir F 18 amyloid PET and 36-month cognitive decline: a prospective multicenter study[END_REF] and the latter being considered less likely to be on the AD track [START_REF] Albert | The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF]. Both studies showed that Aβ+ and Aβ-MCI showed significant atrophy of the CA1 subfield. The effect appeared stronger in the Aβ+ patients but the difference between both patient groups was not significant and might simply reflect a difference in the sample size and associated statistical power (due to a higher number of Aβ+ than Aβpatients). Although these studies should be interpreted as preliminary because of their small sample sizes, they found similar results despite different methods: shape analysis [START_REF] Ye | Hippocampal and cortical atrophy in amyloid-negative mild cognitive impairments: comparison with amyloid-positive mild cognitive impairment[END_REF] and manual volumetry on high-resolution images (La [START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF]. Pending replication in future studies, this suggests that the preferential pattern of subfield atrophy observed in AD is independent from Aβ pathology.

Studies have also shown that CA1 and subiculum atrophy was present at a very early stage of AD, as it was detectable in cognitively healthy individuals that later developed MCI or AD (Apostolova et al., 2010b;[START_REF] Csernansky | Preclinical detection of Alzheimer's disease: hippocampal shape and volume predict dementia onset in the elderly[END_REF]. Together with studies mentioned above showing an association between CA1 (and subiculum) atrophy and subsequent conversion from MCI to dementia, these reports stress the importance of hippocampal subfield measurements as sensitive markers of AD processes and emphasize their potential role as biomarkers for early AD detection.

It should be noted that studies using the FreeSurfer 5.3 automatic method reported discrepant findings as compared to the studies mentioned above that used different techniques. Thus, all FreeSurfer 5.3 studies have detected volume loss in the subiculum and the CA2/3 region in MCI and AD patients, while only a few articles [START_REF] Khan | Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts[END_REF][START_REF] Li | Discriminative analysis of mild Alzheimer's disease and normal aging using volume of hippocampal subfields and hippocampal mean diffusivity: an in vivo magnetic resonance imaging study[END_REF] (Iglesias et al., 2015).

Cognitively intact individuals at risk for AD

To date, only a few studies have assessed hippocampal shape or subfield volumes in cognitively intact individuals at risk for AD, including carriers of the ε4 allele of the Apolipoprotein E, healthy controls with amyloid β deposition, and individuals with isolated subjective memory complaint.

The ε4 allele of the apolipoprotein E (APOE4) is the largest genetic risk factor for sporadic AD [START_REF] Corder | Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families[END_REF][START_REF] Farrer | Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium[END_REF][START_REF] Genin | APOE and Alzheimer disease: a major gene with semi-dominant inheritance[END_REF], and its detrimental effect (as compared to the more frequent ε3 allele) is dose-dependent: odds ratio for AD are 3.2 for ε4/ε3 carriers, and 14.9 for ε4/ε4 carriers [START_REF] Farrer | Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium[END_REF]. Many studies have assessed the effect of APOE polymorphism on grey matter volume but only a few have examined the effects on hippocampal subfields. Overall, the findings converge to a detrimental effect on the cortical thickness or volume of the hippocampus in elderly and patients, although results diverged with regard to the affected subfield (see Table 2). A first group showed that cortical thickness was found to be lower in elderly APOE4 carriers compared with non-carriers in the subiculum [START_REF] Burggren A | Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers[END_REF]. In a subsequent study from the same group, Donix et al.

(2010b) found that family history of AD and APOE4 status were independently associated with a thinner subiculum, with an additive effect of these two AD risk factors. In a longitudinal study of subfield thickness over two years, the same authors showed a stronger rate of atrophy in the subiculum (Donix et al., 2010b). By contrast, [START_REF] Kerchner | APOE ε4 worsens hippocampal CA1 apical neuropil atrophy and episodic memory[END_REF] found a selective, dose dependent effect on the CA1-SRLM width (yet, controls were pooled with MCI and demented patients in this analysis), while Mueller et al. reported a detrimental effect of the ε4 allele on CA3/DG volume in healthy elderly, but not in middle aged individuals [START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF][START_REF] Mueller | Selective effect of Apo e4 on CA3 and dentate in normal aging and Alzheimer's disease using high resolution MRI at 4 T[END_REF]. Overall, the important variability in these results makes it difficult to get a clear picture of the preferential effect of APOE4 on hippocampal subfield volume in asymptomatic individuals. The same discrepancy is found amongst studies assessing cortical atrophy at large (see Fouquet et al., 2014 for review).

Hippocampal subfield atrophy has also been assessed in another group of asymptomatic elderly at-risk for AD, namely, cognitively normal individuals with detectable Aβ pathology evidenced with either PET imaging or cerebro-spinal fluid measurements. Although these individuals are often considered as being in the first stage of the disease [START_REF] Sperling | Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF], others prefer to refer to them as "asymptomatic at risk for AD" [START_REF] Dubois | Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria[END_REF][START_REF] Dubois | Revising the definition of Alzheimer's disease: a new lexicon[END_REF] as it is still unsure whether Aβ pathology alone is sufficient to later develop clinical AD. The presence of Aβ was associated with significantly smaller hippocampal tail, presubiculum and subiculum volumes estimated with FreeSurfer 5.3 [START_REF] Hsu | Amyloid Burden in Cognitively Normal Elderly is Associated with Preferential Hippocampal Subfield Volume Loss[END_REF]. Using radial atrophy measurements and cerebro-spinal fluid measures, Apostolova et al. (2010a) did not identify any specific pattern of shape deformation associated with Aβ pathology. Using another type of shape analysis, [START_REF] Carmichael | Localized hippocampus measures are associated with Alzheimer pathology and cognition independent of total hippocampal volume[END_REF] showed that, in healthy elders, Aβ was related to subtle hippocampal shape patterns, mainly inward deformations in the inferioranterior head as well as the superior and inferior body; however, these morphological changes were not interpreted in terms of subfield atrophy.

Lastly, to date, only one study has assessed patients with subjective memory complaint, known to be at-risk of later developing AD although they perform in the normal range on neuropsychological tests [START_REF] Jessen | AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimer's & dementia : the journal of the Alzheimer[END_REF]. In their article, [START_REF] Tepest | Hippocampal surface analysis in subjective memory impairment, mild cognitive impairment and Alzheimer's dementia[END_REF] reported a slight inward deformation restricted to CA1 as compared to non-complaining healthy controls. This pattern was qualitatively similar to AD and MCI patients but did not reach the level of significance.

Clinical interest of hippocampal subfield volumetry?

MRI as a positive diagnostic tool for AD.

According to the 30 year-old NINCDS-ADRDA criteria for AD that are still currently used in most clinical settings, imaging could be used to exclude potential organic causes of symptoms, such as subdural hematoma, brain tumor, hydrocephalus and dementia associated with vascular disease [START_REF] Mckhann | Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease[END_REF]. However, at the time these criteria were developed, the neuroimaging literature was still limited and authors foresaw that "information should soon be available about the usefulness of MRI in the diagnosis of Alzheimer's disease". Indeed, after more than 2 decades of intense research, it has been proposed to use (MR and PET) imaging to highlight the presence of specific abnormalities, indicating a neurodegenerative process consistent with AD [START_REF] Albert | The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF][START_REF] Dubois | Revising the definition of Alzheimer's disease: a new lexicon[END_REF][START_REF] Dubois | Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria[END_REF][START_REF] Mckhann | The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF][START_REF] Sperling | Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer[END_REF], although experts are still debating about it [START_REF] Dubois | Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria[END_REF].

In this context, hippocampal volumetry appears as a natural candidate, and has therefore already been used in many research studies as a biomarker indicating AD-related degeneration at different stages of disease progression (eg. [START_REF] Jack | Shapes of the trajectories of 5 major biomarkers of Alzheimer disease[END_REF][START_REF] Knopman | Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease[END_REF][START_REF] Wirth | Associations between Alzheimer disease biomarkers, neurodegeneration, and cognition in cognitively normal older people[END_REF]. Recently, several experts have joined forces to establish a standard protocol for hippocampal segmentation [START_REF] Boccardi | Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance[END_REF](Boccardi et al., , 2013a(Boccardi et al., , 2013b[START_REF] Boccardi | Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol[END_REF][START_REF] Bocchetta | Harmonized benchmark labels of the hippocampus on magnetic resonance: The EADC-ADNI project[END_REF][START_REF] Frisoni | The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: Evidence of validity[END_REF], in order to solve the major issue of high variability in hippocampus boundary definition and delineation procedure that limited inter-study comparability from previous works [START_REF] Boccardi | Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol[END_REF]Geuze et al., 2005b;[START_REF] Konrad | Defining the human hippocampus in cerebral magnetic resonance images--an overview of current segmentation protocols[END_REF]. However, whole hippocampal volumetry is likely imperfect because of its rather low sensitivity and specificity [START_REF] Frisoni | The clinical use of structural MRI in Alzheimer disease[END_REF], fueling the need for more accurate disease biomarkers, including subfield volumetry.

Subfields versus whole hippocampus: Improving AD detection?

To date, only a few studies have assessed the diagnostic value of high-resolution subfield volumetry and compared it to the classical whole hippocampus volumetry, but their results are highly consistent.

The first report by [START_REF] Mueller | Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease[END_REF], showed that the CA1-2 transition area was superior to total hippocampal volume for distinction between 53 healthy controls and 20 patients with MCI: the area under the receiver operating characteristic curve (AUROC) was 0.83 for CA1/2 versus 0.72 for the entire hippocampus. Interestingly no subfield showed a significantly higher power than the whole hippocampus to discriminate the controls from 18 AD patients (AUROC= 0.81 for CA1/2 versus 0.75 for whole hippocampus, non-significant difference), suggesting that the superior discriminant capacity of hippocampal subfield volumetry over the whole hippocampus is limited to the predementia stage. Using a semiautomated method, [START_REF] Pluta | In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI[END_REF], showed that the left CA1 volume better discriminated 17 amnestic MCI patients from 28 controls than the total hippocampus (AUROC = 0.84 versus 0.76, p=0.03). The same conclusion was also reached in an independent study with a fully manual delineation technique showing that CA1 was more informative than the total hippocampus to distinguish 17 amnestic MCI patients from 40 elderly controls (AUROC = 0.88 versus 0.76, p=0.05; La [START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF]. In this study and similar to [START_REF] Mueller | Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease[END_REF], the superiority of subfield volumetry over total hippocampal measurement was not found when comparing AD patients to controls (AUROC = 0.92 for CA1 versus 0.91 for total hippocampus, non significant).

Altogether, these studies point to the increased sensitivity of subfield volumetry, and especially the CA1 or CA1/2 area, to detect morphological changes at the prodromal stage of MCI, but not at the dementia stage. This is consistent with the notion that NFT pathology, synaptic, neuronal and volumetric losses are focal in the first pathophysiological stages and progressively extend to the whole hippocampus to become global in the advanced stage of dementia (see section 3.2.1 above).

Subfield volumetry in a clinical setting?

It should be noted that these encouraging results emanate from monocentric studies mostly based on small groups of individuals and should therefore be considered with caution.

This issue is to be addressed through the recent addition of a high-resolution hippocampalcentric T2 acquisition in a third of the sites that participate in the Alzheimer's Disease

Neuroimaging Initiative (ADNI), a large multicentric longitudinal study of AD [START_REF] Mueller | Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimer's & dementia : the journal of the Alzheimer[END_REF].

In addition, these academic studies are based on highly selected individuals: both healthy controls and patients are usually free of major comorbidities and have limited history of psychiatric or cardiovascular disorders. This misrepresentation of the general population could constitute a bias in the assessment of diagnostic utility of hippocampal subfield volumetry, as recent reports have suggested that factors/conditions such as smoking [START_REF] Durazzo | Interactive effects of chronic cigarette smoking and age on hippocampal volumes[END_REF], hypertension [START_REF] Shing | Hippocampal subfield volumes: age, vascular risk, and correlation with associative memory[END_REF], multiple sclerosis [START_REF] Sicotte | Regional hippocampal atrophy in multiple sclerosis[END_REF], borderline personality disorder [START_REF] Rossi | Volumetric and topographic differences in hippocampal subdivisions in borderline personality and bipolar disorders[END_REF] or depression [START_REF] Huang | Structural changes in hippocampal subfields in major depressive disorder: a high-field magnetic resonance imaging study[END_REF]Wisse et al., 2015a) could have an impact on subfield volume, and notably on the subfields known to me the most sensitive to AD (CA1, CA1-2 areas). The higher prevalence of such factors in the general population as compared to these highly selected academic studies would probably increase the variability of subfield volumes and could lower the diagnostic interest of subfield imaging.

Similarly, the diagnostic value of subfield volumetry has only been assessed by comparing MCI or AD patients to healthy controls, without including other groups of non-AD demented patients. Therefore, the context of these studies strongly differ from clinical settings where differential diagnosis can be challenging -memory impairment / dementia can de due to various potential etiologies [START_REF] Arlt | Non-Alzheimer's disease-related memory impairment and dementia[END_REF] -and for which biomarkers could be very helpful.

Indeed, it is well known that total hippocampal atrophy is found in multiple conditions [START_REF] Fotuhi | Modifiable factors that alter the size of the hippocampus with ageing[END_REF]Geuze et al., 2005a), which makes it poorly specific to AD pathophysiological processes. Unfortunately, there is no current data supporting the idea that subfield volumetry would be more specific to AD than total hippocampal volumetry. Indeed, neuroimaging studies in other disorders are sparse and seem to indicate that CA1 (+/subiculum) is also the subfield of strongest atrophy in dementia with Lewy body [START_REF] Chow | Comparing hippocampal atrophy in Alzheimer's dementia and dementia with lewy bodies[END_REF][START_REF] Sabattoli | Hippocampal shape differences in dementia with Lewy bodies[END_REF], and various subtypes of frontotemporal lobar degeneration (La [START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] Lindberg | Hippocampal shape analysis in Alzheimer's disease and frontotemporal lobar degeneration subtypes[END_REF]. Similarly, neuropathological studies have reported a strong CA1 neuronal loss in patients with vascular dementia [START_REF] Gemmell | Hippocampal neuronal atrophy and cognitive function in delayed poststroke and aging-related dementias[END_REF][START_REF] Kril | Patients with vascular dementia due to microvascular pathology have significant hippocampal neuronal loss[END_REF].

From a more practical point of view, the clinical feasibility of high-resolution hippocampus-centric MRI acquisitions is still uncertain because these scans are particularly prone to motion artifacts (for discussion, see La [START_REF] Joie | Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia[END_REF][START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF].

Lastly, the clinical use of such data would only be conceivable with automated and validated subfield segmentation tools that would not require the expertise and time of manual delineation.

To summarize, current data is limited but suggests that subfield volumetry improves the ability to detect AD-related changes, especially in early (predementia) stages of the disease.

However, additional studies conducted in larger and more representative populations are needed to confirm the feasibility and the added value of the technique in a clinical setting.

Healthy aging

Several studies on brain structure, including but not restricted to the hippocampus, have compared the effects of AD, as an example of pathological aging, to the effects of "normal", "healthy", or "successful" aging, generally defined as the age-related changes that occur in the absence of significant cognitive disorder, i.e. disease-free aging. This is usually assessed by comparing the brain structure of young versus old cognitively intact individuals, assessing the correlation between brain volume and age within a group of cognitively healthy individuals, or in longitudinal studies of individuals who remain free of cognitive deficit over a few years.

Several neuroimaging studies have indicated that the human brain shrinks with age, and that brain shrinkage is selective and differential, not uniform or randomly distributed [START_REF] Raz | Differential aging of the brain: patterns, cognitive correlates and modifiers[END_REF][START_REF] Sowell | Mapping cortical change across the human life span[END_REF]. Evidence also suggests that these structural changes explain some part of age-related changes in cognition (see [START_REF] Fjell | Structural brain changes in aging: courses, causes and cognitive consequences[END_REF][START_REF] Lockhart | Structural imaging measures of brain aging[END_REF] for recent reviews of the field).

However, the frontier between "normal" and "pathological" aging is not clear-cut as it is challenging to distinguish age-related changes from the effects of preclinical diseases, especially as the main risk factor for AD is advancing age (see Fjell et al., 2014a for a critical review on these concepts).

Studying aging: methodological considerations

Differences in reported results are frequent as cohorts and methods used to study aging greatly vary among studies. The most optimal way to assess genuine age-related changes is to perform longitudinal studies in which the effect of age is examined within subjects over time [START_REF] Dotson | Depressive symptoms and brain volumes in older adults: a longitudinal magnetic resonance imaging study[END_REF][START_REF] Driscoll | Longitudinal pattern of regional brain volume change differentiates normal aging from MCI[END_REF][START_REF] Fjell | One-year brain atrophy evident in healthy aging[END_REF][START_REF] Marcus | Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults[END_REF][START_REF] Raz | Regional brain changes in aging healthy adults: general trends, individual differences and modifiers[END_REF][START_REF] Raz | Differential aging of the medial temporal lobe: a study of a five-year change[END_REF][START_REF] Resnick | Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain[END_REF][START_REF] Scahill | A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging[END_REF][START_REF] Thambisetty | Longitudinal changes in cortical thickness associated with normal aging[END_REF].

Unfortunately, such studies are rare -because of their cost and attrition bias -and not yet capable of addressing age-related brain differences over several decades. As a consequence, cross sectional studies are more often conducted in order to estimate age-related changes, although they might produce inconsistent results when compared to longitudinal analyses [START_REF] Pfefferbaum | Cross-sectional versus longitudinal estimates of agerelated changes in the adult brain: overlaps and discrepancies[END_REF]. Indeed, the cross-sectional approach is easier to implement, but it suffers from major flaws including potential cohort bias and the influence of elder individuals at the presymptomatic stage of a neurodegenerative disorder which may cause an overestimation of the age effect [START_REF] Burgmans | The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain[END_REF].

The considered age range is also a source of heterogeneity between studies [START_REF] Walhovd | Consistent neuroanatomical age-related volume differences across multiple samples[END_REF] as some studies evaluate the effect of age over the entire adult lifespan [START_REF] Mueller | Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T[END_REF][START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF][START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF][START_REF] Joie | Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence[END_REF]Ziegler et al., 2010 ;[START_REF] Raz | Volume of the hippocampal subfields in healthy adults: differential associations with age and a pro-inflammatory genetic variant[END_REF][START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF][START_REF] Pereira | Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI[END_REF], while others only included elderly people [START_REF] Wang | Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging[END_REF][START_REF] Wang | Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type[END_REF][START_REF] Frisoni | Mapping local hippocampal changes in Alzheimer's disease and normal ageing with MRI at 3 Tesla[END_REF][START_REF] Apostolova | Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease[END_REF]Wisse et al., 2014b ;[START_REF] Khan | Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts[END_REF]. This point is particularly important as the effect of age on brain structures is known to be non-linear, with a strong regional specificity in the dynamic of the effects [START_REF] Sowell | Mapping cortical change across the human life span[END_REF]Fjell et al., 2014). Moreover, most studies only investigated linear associations between age and subfield volumes, while quadratic or more complex models were used in only a few studies [START_REF] Mueller | Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T[END_REF][START_REF] Ziegler | Brain structural trajectories over the adult lifespan[END_REF][START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF][START_REF] Yang | Evolution of hippocampal shapes across the human lifespan[END_REF].

The aging subfields

Table 3 summarizes the main results from studies on the effect of age on subfield structure.

The association between hippocampal subfield integrity and age was initially evaluated in postmortem studies. [START_REF] West | Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer's disease[END_REF] described a linear effect of age on the hilus (dentate gyrus) and subiculum, with an average loss of 37% and 43% respectively from 13 to 101 years, while another study showed a linear loss of neurons for CA1 (67%) and subiculum (32%) from 16 to 99 years [START_REF] Simic | Volume and number of neurons of the human hippocampal formation in normal aging and Alzheimer's disease[END_REF]. Some researchers did not find any neuronal loss with age [START_REF] Price | Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease[END_REF][START_REF] Rössler | Stage-dependent and sector-specific neuronal loss in hippocampus during Alzheimer's disease[END_REF], probably because the age range considered was narrow, only including elderly people (60 to 89 years and 58 to 88 years, respectively).

The first in vivo neuroimaging study used the high-dimensional mapping method to assess shape differences between young and elder adults, showing areas of both inward (in the head and tail) and outward (in the body) deformation with increased age. Changes did not seem to be subfield-specific and rather suggested a general flattening of the whole structure [START_REF] Csernansky | Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Dementia of the Alzheimer type[END_REF]). Yet, using the same method to assess longitudinal changes after a 2 year follow up in elder adults, the authors found exclusively inward surface deformations (ie.

underlying tissue shrinkage) along the medial part of the hippocampus (i.e. the subiculum)

and in the hippocampal head [START_REF] Wang | Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging[END_REF]. Interestingly, another study found an effect on the head and subiculum with an accelerated deformation in adults over 63 years old [START_REF] Yang | Evolution of hippocampal shapes across the human lifespan[END_REF]. Amongst studies using radial atrophy in elderly people, two showed a global effect of age on all subfields [START_REF] Wang | Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type[END_REF][START_REF] Apostolova | Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease[END_REF], while one reported an significant effect restricted to CA1 and the presubiculum [START_REF] Frisoni | Mapping local hippocampal changes in Alzheimer's disease and normal ageing with MRI at 3 Tesla[END_REF].

Using a VBM-based method, [START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF] found a linear association between age and the volume of the infero-medial part of the hippocampal surface mainly corresponding to the subiculum, and this result was reproduced on a different sample using the same method [START_REF] Joie | Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence[END_REF]. Thomann et al. (2012) also described a strong effect on the subiculum, as well as on the cornu ammonis (all CA subfields were grouped in the same region of interest) with a comparable method. Interestingly, [START_REF] Ziegler | Brain structural trajectories over the adult lifespan[END_REF] used VBM with subfield probability maps [START_REF] Amunts | Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps[END_REF] on a very large sample of cognitively normal individuals (n = 547, from 19 to 86 years old) to model the dynamic of volume loss across the lifespan. They showed that the subiculum volume decreased linearly across the adult lifespan while the volume of the other subfields was approximately stable in early life, before dropping dramatically beginning around age 60.

The first subfield volumetric studies were based on manual delineation performed on the hippocampus body. [START_REF] Mueller | Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T[END_REF] found a linear effect in CA1 when comparing young to elder adults. In a subsequent study including more individuals, they also found an effect on CA3/DG [START_REF] Mueller | Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields[END_REF]. Another group found linear atrophy in the CA1/2 area [START_REF] Shing | Hippocampal subfield volumes: age, vascular risk, and correlation with associative memory[END_REF][START_REF] Raz | Volume of the hippocampal subfields in healthy adults: differential associations with age and a pro-inflammatory genetic variant[END_REF]. [START_REF] Kerchner | Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: a seven tesla magnetic resonance imaging study[END_REF] and no significant changes in the other subfields; this pattern of subfield atrophy dynamic across the lifespan was almost identical to the one reported by Ziegler et al (2010). In an independent sample of 29 elderly between 65 and 80 years old, Wisse et al. (2014b) reported a significant age-related volumetric decrease in CA1 and DG&CA4, with annual atrophy rates of 1.4% and 2.4%, respectively. Finally, using the automated FreeSurfer 5.3 method on a cohort of individuals between 50 and 75 years old, a recent study showed a linear effect of age on CA2/3 and CA4/DG [START_REF] Pereira | Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI[END_REF].

As mentioned above, longitudinal studies of hippocampal subfields in normal aging are rare and so far unconclusive. [START_REF] Das | Measuring longitudinal change in the hippocampal formation from in vivo highresolution T2-weighted MRI[END_REF] showed that high-resolution structural imaging of hippocampal subfields was a viable modality for longitudinal analysis in spite of the technical complexity, partly due to voxel anisotropy of dedicated high-resolution images.

Interestingly, when studying 25 cognitively normal elders, no significant longitudinal atrophy was detected in any hippocampal subfield, potentially because of the limited sample size and short follow-up duration. Moreover, Donix et al. (2010a) found that non-APOE4 carriers did not show significant thinning in any subfield over two years whereas APOE4 carriers showed decrease in cortical thickness in all subregions except CA23-DG.

Cognitive correlates of subfield atrophy in aging and dementia

Although the involvement of the hippocampus in episodic memory is irrefutable, it is still unclear if and how each of its subfields potentially contributes to different aspects of memory Indeed, Mueller et al. (2011) assessed correlations between subfield volumes and two different memory scores derived from the California Verbal Learning Test (CVLT) in a mixed group including cognitively intact healthy controls with a subjective memory complaint and patients with cognitive deficits (mostly amnestic MCI patients). They showed that CA3/DG volume was related to scores assessing verbal learning and early retrieval while CA1 atrophy was correlated with impaired consolidation/delayed retrieval. Contrastingly, strong correlations were also described in AD patients between delayed recall performance and the widths of CA1-SRLM and CA1-SP, while DG/CA3 size did not significantly correlate with any aspect of memory performance [START_REF] Kerchner G A | Hippocampal CA1 apical neuropil atrophy and memory performance in Alzheimer's disease[END_REF]. In a larger and mixed group (controls, MCI and AD patients), [START_REF] Kerchner | APOE ε4 worsens hippocampal CA1 apical neuropil atrophy and episodic memory[END_REF] replicated their first finding of a CA1-SRLM to delayed recall association.

Using surface-based shape or VBM-based analyses, studies have highlighted a strong association between CA1 atrophy and i) impaired free and total (free + cued) recall of verbal information in AD patients [START_REF] Sarazin | The amnestic syndrome of hippocampal type in Alzheimer's disease: an MRI study[END_REF]; and ii) impaired recognition performances, likely reflecting encoding deficits in amnestic MCI patients [START_REF] Fouquet | Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild Cognitive Impairment[END_REF].

Accordingly, atrophy of the CA subfields (grouped together in a single region) seem to account for associative memory deficits observed in MCI patients [START_REF] Atienza | Associative memory deficits in mild cognitive impairment: the role of hippocampal formation[END_REF], and correlates with patients' inability to benefit from semantic processing while encoding new information. Looking at cognitive correlates of hippocampal radial atrophy, associations have been found between impaired delayed recall and CA1 and subiculum volumes in MCI (Apostolova et al., 2006b). In an equivalent analysis run on 490 individuals (including controls, MCI and AD patients from the Alzheimer's disease neuroimaging initiative), authors did not identify specific correlates in healthy controls, while areas corresponding to CA1 and subiculum were associated with delayed recall performances in MCI, and to a lesser extent in AD patients (Apostolova et al., 2010a).

A few groups have assessed memory-structure correlations in healthy controls. In healthy young adults, [START_REF] Chadwick | CA3 size predicts the precision of memory recall[END_REF] found that CA3 size predicted the precision of memory recall, assessed as the ability to distinguish memories with a high degree of similarity. In healthy adults between 52 and 82 years old, [START_REF] Bender | Vascular risk moderates associations between hippocampal subfield volumes and memory[END_REF] replicated a previous work [START_REF] Shing | Hippocampal subfield volumes: age, vascular risk, and correlation with associative memory[END_REF], with larger CA3-4/dentate gyrus volume being associated with better associative memory. In this later study, CA1-2 volumes were also specifically associated with free recall of common nouns, but only in hypertensive individuals [START_REF] Bender | Vascular risk moderates associations between hippocampal subfield volumes and memory[END_REF].

Overall, studies reporting subfield volume / memory correlations in aging and dementia have led to variable, and sometimes contradicting results. This variability is likely due to the studied samples (e.g. degree of memory impairment, whether patients and controls are pooled together, etc), but also to the important variations in both the cognitive (sub)tests and neuroimaging measurements used in the different studies. However, a couple of consistent results seem to emerge: i) the general link between CA3-4-DG volume and associative memory abilities, which is consistent with the implication of these structures in pattern separation and pattern completion (see [START_REF] Rolls | The mechanisms for pattern completion and pattern separation in the hippocampus[END_REF][START_REF] Yassa | Pattern separation in the hippocampus[END_REF] for review) and, ii) the role of CA1 atrophy in memory deficits in MCI and AD, although it is not clear whether this focal atrophy relates to specific memory components. However, these finding emanate from a limited number of labs, and are therefore derived from very specific methods (e.g. subfield definition, image acquisition, cognitive tests, etc) and need further confirmation.

More generally, it is to be acknowledged that cognitive scores, especially when derived from standard clinical memory tests, do not purely reflect specific processes but should rather be seen as composite indices that combine multiple processes of memory. This notion therefore suggests one should remain cautious when interpreting these correlations between distinct memory tests and subfields.

Conclusion and future directions

In the present review, we gave an overview of the numerous neuroimaging approaches used to assess age-and AD-related hippocampal subfield structural changes in humans. We showed that this rapidly growing field, relying on major technical advances in terms of image acquisition and analysis, has succeeded in providing reliable in vivo measures of hippocampal subfields, as suggested by the similarity of the findings with neuropathological data and the progressive pattern of hippocampal subfield atrophy evidenced over the course of AD.

However, some questions are still unanswered because of the limited number of studies or due to the strong contradictions between studies, especially regarding the effects of APOE4 or age on hippocampal subfields (see Table 2 and3, respectively).

Thus, almost all studies have identified region-specific effects of pathology (at least in the earliest stages of the disease), or region-specific volume-cognition correlations, but there are strong discrepancies regarding the targeted subfield(s), most likely because of differences in the terminology and segmentation protocols used in the various laboratories working in the field. The ongoing effort for standardizing segmentation protocols led by experts in the field gathered in the Hippocampal Subfield Group (www.hippocampalsubfields.com) is aimed at addressing these issues, and has already led to a first study comparing 21 segmentation protocols, including most of those used in the study of aging and AD (Yushkevich et al., 2015a). Interestingly, this between-protocol comparison identified the CA1/subiculum border as one of the most discrepant features among segmentation protocols. As this boundary is thought to be the earliest areas of NFT pathology within the hippocampus [START_REF] Lace | Hippocampal tau pathology is related to neuroanatomical connections: an ageing population-based study[END_REF], the definition of this boundary in neuroimaging studies might have a significant impact on the detection of the subfield of greatest atrophy (i.e. CA1 or the subiculum). This protocol comparison was the first step of an effort to harmonize and enable comparisons between protocols; a white paper for harmonization has been developed

(www.hippocampalsubfields.com/whitepaper) to plan for next steps (for further discussion, see Yushkevich et al.,2015a) Moreover, while some effects have been highlighted by the vast majority of studies (e.g.

CA1 atrophy in early AD), it is still uncertain what are the histological phenomena driving this macroscopic volume reduction (neuronal loss, neurite loss, neuronal shrinkage, changes in glial cells, etc?). Data from ex vivo MRI or pre mortem, in vivo MRI combined with histological analysis of the medial temporal lobe would highly benefit the field. The emergence of PET ligands for tau pathology [START_REF] Chien | Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T808[END_REF][START_REF] Maruyama | Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls[END_REF][START_REF] Okamura | Non-invasive assessment of Alzheimer's disease neurofibrillary pathology using 18F-THK5105 PET[END_REF] will also allow us to question the links between hippocampal subfield atrophy and the development of tau pathology in both aging and AD. Specifically, it will give a unique opportunity to assess whether tau pathology, which appears in early/mid-life even in cognitively normal individuals [START_REF] Braak | Diagnostic criteria for neuropathologic assessment of Alzheimer's disease[END_REF][START_REF] Braak | The pathological process underlying Alzheimer's disease in individuals under thirty[END_REF], underlies hippocampal subfield atrophy in both aging and dementia or whether the pattern or subfield atrophy observed in aging is independent of tau.

Lastly, hippocampus-related disorders cannot be solely studied from the interesting but narrow perspective of structural imaging, and it is certain that multimodal imaging would provide additional and complementary information on the cerebral basis of cognitive disorders [START_REF] Hedden | Challenges and Opportunities in Linking Brain-Based Biomarkers to Person-Specific Variation in Cognition: Pumping Up the Volume[END_REF]La Joie et al., 2014b;[START_REF] Maruszak | Why looking at the whole hippocampus is not enough-a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer's disease diagnosis[END_REF]. In the coming years, the combined use of subfield-centered task-related functional MRI [START_REF] Maass | Laminar activity in the hippocampus and entorhinal cortex related to novelty and episodic encoding[END_REF][START_REF] Suthana N A | Reduced hippocampal CA2, CA3, and dentate gyrus activity in asymptomatic people at genetic risk for Alzheimer's disease[END_REF]Yassa et al., 2010;see Carr et al., 2010 for review), restingstate functional MRI protocols [START_REF] Das | Increased functional connectivity within medial temporal lobe in mild cognitive impairment[END_REF][START_REF] Libby | Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging[END_REF], FDG-PET [START_REF] Cho | Substructural hippocampal glucose metabolism observed on PET/MRI[END_REF], and diffusion tensor imaging assessing either white matter fiber tracks (Yassa et al., 2010a;[START_REF] Zeineh | Ultra-high resolution diffusion tensor imaging of the microscopic pathways of the medial temporal lobe[END_REF]Wisse et al., 2015b) or grey matter microstucture in subfields [START_REF] Yushkevich | Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment[END_REF] and will likely aid in understanding the multiple levels of hippocampal alteration over the course of AD [START_REF] Leal | Perturbations of neural circuitry in aging, mild cognitive impairment, and Alzheimer's disease[END_REF].

Figures.

Figure 1. Surface-based methods to assess h ippocampal subfield atrophy in AD.

The figure illustrates the pattern of hippocampal subfield atrophy in AD (as compared to healthy controls) evidenced using three different methods. For the sake of simplicity, only the right hippocampus is presented (from a superior view). Top row shows the atlas used by each group to determine the localization of subfield on the hippocampal surface while bottom row shows the pattern of atrophy in AD. All three methods show a strong atrophy along the lateral zone of the hippocampus, corresponding to the CA1 subfield.

  described a diminution of the entorhinal cortex and CA1-SRLM width in older adults compared to their younger counterparts. In other studies, the manual delineation covered almost the whole hippocampus. Thus, La Joie et al. (2010) described a linear effect of age on the subiculum with a relative preservation of CA1 and CA2/3/4/DG pooled together when considering changes in a group of individuals between 19 and 68 years old. In a follow-up study including the same 50 adults together with another 48 new individuals, de Flores et al. (2015) observed a linear decrease of the volume of the subiculum, a nonlinear decrease of CA1 volume dropping around 50 years,

Figures

  Figures are reproduced from: (left column) Wang et al., Neuroimage 2003, with permission from Elsevier ; (middle column) Frisoni et al., Neuroimage (2006), with permission from Elsevier ; (right column) Chételat et al., Neuropsychologia (2008), with permission from Elsevier.

  also reported CA1 atrophy (See Table1, bottom section). As mentioned above, this might be due to difference in subfield definition and boundaries used in FreeSurfer 5.3. More specifically, FreeSurfer 5.3's CA1 is smaller than in other protocols, and most of FreeSurfer 5.3's subiculum overlaps with what other protocols consider as CA1 (see[START_REF] De Flores | Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry[END_REF] Wisse et al., 2014a for further discussion). On the contrary, the new method implemented in Freesurfer 6.0 seem to produce more coherent results, CA1 being one of the most strongly different subregions when considering MCI or AD patients compared to controls

  function. Different approaches have been used to address this question, including highresolution functional MRI (see Carr et al., 2010 for review) in healthy and impaired individuals, but also volume-cognition correlational analyses, aimed at identifying the structural correlates of inter-individual variability in cognition or the underpinnings of specific memory deficits in impaired patients.

who hesitated between the terms "sea horse" and "silkworm", see[START_REF] Duvernoy | The Human Hippocampus, Functional Anatomy, Vascularization and Serial Sections with MRI, Third edit[END_REF] and El-[START_REF] El-Falougy | History, anatomical nomenclature, comparative anatomy and functions of the hippocampal formation[END_REF] for further details on the nomenclature and its evolution.

However, there are still controversies regarding the existence of CA4 as a distinct entity ; according to some authors, these neurons probably belong to the hilar region of CA3 (for discussion see[START_REF] Schultz | Anatomy of the hippocampal formation[END_REF]

Acknowledgements

The authors are grateful for the financial support they have received from Association France Alzheimer ; Fondation Plan Alzheimer (Alzheimer Plan 2008-2012) ; Programme Hospitalier de Recherche Clinique (PHRC National 2011) ; Agence Nationale de la Recherche (ANR LONGVIE 2007) ; Région Basse Normandie ; Institut National de la Santé et de la Recherche Médicale (INSERM) including Ecole de l'Inserm-Liliane Bettencourt ; fondation Philippe Chatrier and fondation Thérèse et René Planiol pour l'étude du cerveau.

We thank Jacob W Vogel for his comments and English editing as well as Paul Yushkevich for sharing images from his article.

Projection of VBM results onto surface mesh [START_REF] Chételat | Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry[END_REF] Large deformation high dimentional brain mapping [START_REF] Wang | Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging[END_REF] Radial atrophy [START_REF] Frisoni | In vivo neuropathology of the hippocampal formation in AD: a radial mapping MR-based study[END_REF] CA2 The figure shows the legt hippocampus of a 36 yo healthy control acquired with different scanners (1.5T, 3T, 7T) and segmented by mulitple groups (using their own segmentation protocol/atlas) participating in the Hippocampal Subfields Group. The top slice corresponds to the head while the bottom slice shows the body of the hippocampus. The lower panel of each segmentation exemple indicates which substructures were segmented each protocol.

3D-atlas AD-related changes