Differential vulnerability of hippocampal subfields and anteroposterior hippocampal subregions in healthy cognitive aging

Accepted Manuscript Differential vulnerability of hippocampal subfields and antero-posterior hippocampal subregions in healthy cognitive aging N.V. Malykhin, Y. Huang, S. Hrybouski, F. Olsen PII:

S0197-4580(17)30256-7

DOI:

10.1016/j.neurobiolaging.2017.08.001

Reference:

NBA 9991

To appear in:

Neurobiology of Aging

Received Date: 15 September 2016 Revised Date:

1 August 2017

Accepted Date: 2 August 2017

Please cite this article as: Malykhin, N.V., Huang, Y., Hrybouski, S., Olsen, F., Differential vulnerability of hippocampal subfields and antero-posterior hippocampal subregions in healthy cognitive aging, Neurobiology of Aging (2017), doi: 10.1016/j.neurobiolaging.2017.08.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Differential vulnerability of hippocampal subfields and antero-posterior hippocampal

Neuroscience and Mental Health Institute, 2Department of Biomedical Engineering, University

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of Alberta, Edmonton, Alberta, Canada

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Malykhin N.V.1,2*, Huang Y.2, Hrybouski S.1, Olsen F.2.

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subregions in healthy cognitive aging.

*Corresponding author

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Nikolai V. Malykhin M.D., Ph.D., Department of Biomedical Engineering, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada, T6G 2V2 Phone: (780) 2481120 Fax: (780) 492-8259, [email protected]

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Abstract

In present study we investigated whether hippocampal subfields (cornu ammonis 1-3, dentate

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gyrus, and subiculum) and antero-posterior hippocampal subregions (head, body, and tail) follow the same trajectory with age using structural magnetic resonance imaging (MRI).

We recruited 129 healthy volunteers, 18-85 years old. Structural MRI scans were acquired on a

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4.7T system. Hippocampal subfields and subregions were manually segmented using reliable volumetric protocols.

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We found that all effects of age on the hippocampal volumes were non-linear and were mainly found in the hippocampal body, while the hippocampal head and the tail volumes were not associated with age. The total subiculum and the total dentate gyrus volumes were associated with age, while the total cornu ammonis 1-3 was not.

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Significant associations with age for the cornu ammonis 1-3 and the dentate gyrus volumes were present only in the hippocampal body, while the subiculum volumes were associated with age throughout the entire hippocampus. Subiculum volumes were more negatively related to age in

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men than in women.

Keywords: hippocampus; aging; magnetic resonance imaging; dentate gyrus; cornu ammonis; subiculum

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1. Introduction

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The relationships between healthy aging and the hippocampus (HC) have been studied extensively

using volumetric magnetic resonance imaging (MRI) (Allen et al., 2005; Good et al., 2001; Raz et al., 2004; Van Petten, 2004). Given that memory declines with age and because the HC plays a

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critical role in memory, this brain structure might be particularly vulnerable to effects of aging. Extensive body of research supports severe HC atrophy in Alzheimer’s disease (Poulin and

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Zakzanis, 2002), which can be detected on MRI years before the clinical diagnosis (Chételat and Baron, 2003; Whitwell et al., 2007). Unlike dementia research, results of studies on the HC changes in healthy aging have been inconsistent. Some volumetric MRI studies found that the HC volumes decrease with normal aging (Mu et al., 1999; Allen et al., 2005; Walhovd et al.,

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2005; Malykhin et al., 2008a, Raz et al., 2004), while several other studies reported HC preservation (Good et al., 2001; Grieve et al., 2005; Sullivan et al., 1995). In previous crosssectional studies, HC volume reductions were more prominent after 50 years of age (Mu et al.,

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1999; Lupien et al., 2007), and have been found to accelerate in later life (Raz et al., 2004). Most volumetric MRI studies have analyzed the HC as an amalgamated structure (Van Petten,

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2004). The hippocampus can be subdivided along its antero-posterior axis into anatomical segments or sections known as the HC head, body and tail listed from most anterior to most posterior (Duvernoy, 2005). These antero-posterior HC segments are also called HC subregions (Malykhin et al., 2007; Rajah et al. 2010; Daugherty et al., 2015). More recent MRI volumetric methods have been developed to segment the HC into its anteroposterior anatomical subregions and to include the HC tail in the calculation of total HC volume (Maller et al., 2006; Malykhin et al., 2007; Rajah et al. 2010; Daugherty et al., 2015). Although 3

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the precise functions of the antero-posterior HC subregions are still not well understood, it has been suggested that the dorsal (posterior) HC is implicated in memory and spatial navigation and the ventral (anterior) HC mediates anxiety-related behaviours (Bannerman et al., 2004; Strange

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et al., 2014; Poppenk & Moscovitch, 2011). Several earlier volumetric MRI studies suggested that the anterior HC might be relatively preserved in healthy aging (Kalpouzos et al., 2007; Driscol et al., 2003; Malykhin et al., 2008), while other studies reported relative preservation of

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the posterior HC (Pruessner et al., 2001; Rajah et al., 2010).

Apart from antero-posterior subdivisions, the HC has a heterogeneous inner structure that can be

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further delineated in into several histologically-defined subfields, including the Cornu Ammonis (CA1-4), the dentate gyrus (DG), and the subiculum (Sub) (Duvernoy, 2005). Each subfield houses a distinct population of neurons with distinct connectivity profiles, providing a cytological basis for differential vulnerability of each subfields to pathological processes.

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Advances in high-resolution MRI have enabled measurements of the HC subfields in vivo (for overview of 21 segmentation protocols see Yushkevich et al., 2015). The agreement and variability of various segmentation methodologies were recently compared on a common MRI

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dataset (Yushkevich et al., 2015). The study revealed significant variability among the protocols currently used in the field in terms of what labels were used, where the boundaries between

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labels were placed, and what extent of the HC structure was labeled (Yushkevich et al., 2015). Despite different methodology, all HC subfield segmentation techniques relied on various geometrical rules and visibility of the stratum lacunosum-moleculare (SLM), visible on T2weighted MRI scans with sub-millimeter in-plane resolution. Previous cross-sectional volumetric MRI studies of HC subfields in healthy aging (Table 1) examined only 3-5 slices of the HC body (Mueller et al., 2007, 20098; Mueller and Weiner 2009;

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Shing et al., 2011; Raz et al., 2014; Daugherty et al., 2016), excluded parts of the HC from the analysis (La Joie et al. (2010), de Flores et al. (2015); or did not segment the HC subfields within the posterior part of the HC tail (Wisse et al., 2014). Therefore, in addition to differences in MRI

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acquisition and interpretation of the relationships between age and the HC subfield volumes might be affected by the extent to which each HC subfield was measured, age range or presence of pathologies.

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Thus, although aggregate HC volume reduction in normal aging has been well replicated, the detailed underlying structural alterations contributing to the overall volume reduction have yet to

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be specified. Our group was the first to report volumes for the HC subfields across the entire HC formation, and demonstrated differences in distributions of the HC subfields in all anteroposterior HC subregions (Malykhin et al., 2010), potentially suggesting that factors impairing the DG may have their greatest impact on the HC body, while processes that preferentially affect

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CA1-3 subfields may have greater impact on the HC head.

The main goal of the present study was to investigate whether all HC subfields and anteroposterior HC subregions show similar associations with age in a large cohort of healthy

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participants across the entire adult lifespan.

2. Material and methods

2.1. Subjects

We recruited 129 healthy volunteers (59 males, 70 females), 18-85 years old (Mean = 47.6 years, SD = 18.9 years). Of those, 98 participants were Caucasian (76%), 20 Asian (16%), 7

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Latin American (5%), and 4 Arab (3%) Canadians. Participants were recruited through online advertisements, word-of-mouth, and advertising in local community centres. An initial phone interview was conducted to screen candidates for existing neuropsychiatric disorders, as well as

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MRI contraindications. Healthy volunteers had no lifetime psychiatric disorders and no reported psychosis or mood disorders in first-degree relatives, as assessed by the Anxiety Disorders Interview Schedule—IV (Brown et al., 2001). Medical exclusion criteria were defined as those

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active and inactive medical conditions that may interfere with normal cognitive function: cerebrovascular pathology, all tumors or congenital malformations of the nervous system,

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diabetes, multiple sclerosis, Parkinson's disease, epilepsy, organic psychosis, schizophrenia, and stroke, current or past use of psychotropic medications; past or current recreational drug use was also exclusionary. In addition, an in-person interview was conducted to assess the participants’ cognitive abilities. Older subjects with Mild Cognitive Impairment (MCI) and dementia were

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excluded from the study. For exclusion, dementia was defined according to the DSM-IV criteria. MCI was defined by the presence of cognitive complaints (documented on the AD-8, Galvin et al., 2005) with documented impairment on the Montreal Cognitive Assessment Test (MOCA)

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(all subjects had MOCA score ≥26) (Nasreddine et al., 2005). Futhermore, older (>50 years of age) participants were assessed for vascular dementia with the

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Hachinski Ischemic Scale (HIS) (Hachinski et al., 1975). A score above 7 out of 18 has 89% sensitivity. All elderly participants included in this study received a HIS score of 3 or lower. The Clinical Dementia Rating scale (CDR) was used as an assessment of dementia symptom severity (Hughes et al., 1982), where subjects are assessed for functional performance in six areas: memory, orientation, judgement & problem solving, community affairs, home and hobbies, and personal care. We employed CDR as an additional screening measure for dementia

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in older participants. A composite score from 0 to 3 was calculated. All of our subjects met the cutoff score of < 0.5 for total CDR score. In order to screen older participants for depression, the Geriatric Depression Scale (GDS) was

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used (Yesavage et al., 1982). Designed to rate depression in the elderly, a score of > 5 is suggestive of depression and a score > 10 is indicative of depression. Our subjects met the cutoff score of 4 and below.

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University of Alberta Health Research Ethics Board.

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Written, informed consent was obtained from participants and the study was approved by the

2.2. MRI Data acquisition

All images were acquired at the Peter Allen MR Research Centre (University of Alberta, Edmonton, AB) on a 4.7T Varian Inova MRI scanner using a 4-channel phase-array coil. T2-

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weighted Fast Spin Echo (FSE) acquisitions employed contiguous 1 mm thick slices, with echotime (TE) of 39ms, repetition time (TR) of 11000ms, field of view (FOV) of 20x20cm and inplane matrix of 384x296, native resolution of 0.52×0.68×1.0 mm3. 90 slices were obtained

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perpendicular to the anterior–posterior commissure line (AC-PC) in a total acquisition time of 13.5 min. Our slices were oriented perpendicular to the AC–PC line in order to provide better

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delineation between antero-posterior HC subregions and HC subfield delineation based on the anatomical atlas of the human HC (Duvernoy, 2005), where the bicommissural plane (i.e., AC– PC) was also used in both histological and MRI examinations. A whole brain T1-weighted 3D Magnetization Prepared Rapid Gradient-Echo (MPRAGE) sequence [TR: 8.5 ms; TE: 4.5 ms; inversion time: 300 ms; flip angle: 10°; FOV: 256 × 200 × 180 mm3; voxel size: 1 × 1 × 1 mm3] was used to calculate Intracranial Volumes (ICV).

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2.3. MRI Data analysis The program DISPLAY (MNI, Montreal) was used to trace ICVs on the T1-weighted

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MPRAGE images and HC subfields on the T2-weighted FSE images. Detailed reliable MRI segmentation protocols for the total HC volumes, HC subfields’ and antero-posterior HC subregions’ volumes, and ICV were described in detail in our earlier work (Malykhin et al, 2007,

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2010). The HC was manually segmented along the antero-posterior axis into the HC subregions: head, body, and tail (Fig.1) with the uncal apex and separation of the fornix used to define

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subdivision boundaries (Malykhin et al., 2007). These HC subdivisions are based on connectivity, as opposed to cytoarchitectonic criteria, and follow the anatomical parcellation described by Duvernoy (2005). The HC head receives dense projections from the amygdala, the entorhinal cortex and the perirhinal cortex (Witter and Wouterlood, 2001). The major projections

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to the HC body and the HC tail arise from the parahippocampal cortex. In our parcellation method the most posterior slice of the hippocampal head was the first slice where the uncal apex (uncus) was clearly present (Duvernoy, 2005). The coronal plane provided a clear view of this

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significant medial extension. The most anterior HC body slice was the slice just before the appearance of the uncus. The most anterior slice of the HC tail was the first slice where the

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fornix was clearly seen in full profile, or was separated from the wall of the ventricle, whichever came first.

Quantification of the HC subfields within these a priori subdivisions was guided by the

anatomical atlas of the human HC (Duvernoy, 2005). Since the microscopic delineation between HC subfields is not possible without histological examination, we divided the HC into three subfield areas corresponding to our best approximation of the CA areas 1-3 (CA 1-3), the DG

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(DG + CA4, henceforth in text referred to as DG), and the Sub within the HC head, body, and tail (Fig. 1). Measurements of subfield areas started at the level of the HC body (Figs. 1c, f), delineating the DG first, followed by the Sub and theCA1-3. The stratum lacunosum-moleculare

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(SLM) of the CA and the molecular layer of the DG both consist of axons and dendrites with an anteroposterior orientation (Shepherd et al., 2007), which was clearly visible as white matter bordering the DG (Figs. 1b-g). This white matter formed the lateral, medial and inferior

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boundaries of the DG. White matter of the fimbria (Figs. 1c,f) and fornix (Figs. 1d, g) served as superior boundaries for the DG, but were excluded from volumetric analyses. The Sub was

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defined as being located inferior and medial to the DG (Figs. 1e-g). Separation of the Sub from the CA1-3 was defined as the inferior continuation of a line corresponding to its supero-lateral boundary along molecular layer of the DG (Fig. 1f). At the level of the HC head, this line becomes horizontal (Fig. 1 e). Where the DG expanded medially, the SLM surrounded it from all

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sides (Fig. 1 e). Anteriorly, the DG was traced until it was surrounded by the white matter of the SLM This usually corresponded to the third/fourth coronal slice after the amygdala first appeared, when tracing was performed in the posterior to anterior direction. At the level of the

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HC tail, the DG was attached to the fornix and was separated from the CA1-3 by white matter of the SLM, and Sub was located infero-medially to the DG and was present only in the first two

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anterior slices (Fig. 1g).

All measurements were performed by a single rater (YH), trained by the developer of the

protocol (NM). Intra-rater and inter-rater reliabilities for the HC subfield/subregion volumes and the ICV measures were assessed by retracing MRI images from 5 subjects (i.e. 10 hippocampi total) for the HC subfield/subregion volumes, and 10 subjects for the ICV measures at a oneweek interval. Inter (intra) - rater reliability intraclass correlations coefficients (ICCs) for the

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antero-posterior HC subregions were as follows: 0.95 (0.88) for the HC tail, 0.83 (0.93) for the HC body, 0.95 (0.92) for the HC head, and 0.96 (0.86) for the total HC. Statistical significance

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for all ICCs was less than .0005. Inter (intra) - rater reliability ICCs for the HC subfield volumes were 0.92 (0.92) for the CA1-3, 0.86 (0.84) for the DG, 0.87 (0.95) for the Sub subfield, and 0.95 (0.97) for the total HC

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(all ps < .0005). Inter (intra) - rater ICCs for ICV measurements were 0.98 (0.99) (p < .0005). Raw HC volumes were normalized using formula for volume correction we employed in

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Travis et al. (2014): Normalized hippocampal volume = Raw ROI volume (mm3) / ICV of the same subject (cm3) × sample averaged ICV (cm3). We used ICV to normalize the HC volumes because unlike the total brain volume, the ICV does not change with age (Raz et al., 2004,

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Michielse et al., 2011).

2.4. Statistical analysis

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Descriptive statistics for raw and normalized HC subfield and HC subregion volumes were calculated using IBM SPSS Statistics (Version 23 for Mac). The asymmetry index for the

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HC volumes was calculated using the following formula: asymmetry index = ((right volume / left volume) x 100)-100. Since we found no relationships between age and asymmetry indices for any of the HC subregion and subfield volumes (all FWE-corrected ps > .05), left and right HC volumetric data were averaged. Previous research demonstrated that the HC follows non-linear volume decline with age (Allen et al., 2005; Fjell et al., 2013; Raz et al., 2004; Walhowd et al., 2005). Low-order

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regressions impose severe shape restrictions (e.g. line for linear models and a parabola for quadratic models), while higher-order functions may lead to overfitting and poor generalizability. Here, we relied on multi-model inference to overcome those limitations. Multi-

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model inference does not assume that a single model is the ‘optimal’ or ‘true’ fit to the data. Instead, each model receives a likelihood estimate and models are subsequently averaged, based on their likelihoods. All of the ensuing computations were performed using in-house custom-

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written MATLAB (v. 2016b) programs.

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First, we generated a complete set of 16 parameters incorporating age and sex effects up to polynomial order 5 [main effect of sex, main linear/cubic/quadratic/quartic/quintic effect of age, linear/cubic/quadratic/quartic/quintic male interaction, linear/cubic/quadratic/quartic/quintic female interaction]. Next, all possible combinations of these parameters were computed and intercept terms were added to each model. Lastly, a single model containing only the intercept

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term was added to the pruned model set, which resulted in a total of 243 potential polynomial regression models, up to polynomial order 5, with various combinations of sex, age, and

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interactions terms.

Model likelihoods can be estimated using either theoretical approaches or via resampling

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techniques (Burnham and Anderson, 2002). To model the relationships between our HC ROIs and age, we used non-parametric bootstrap to estimate model likelihoods. To achieve this, a total of 10,000 bootstrap samples were generated, and for each bootstrap sample the model with the lowest Constrained Akaike Information Criterion (AICc) score was selected. We relied on a unified model selection procedure, in which males and females were modeled simultaneously. This enabled the AICc algorithm to find an optimal trade-off between bias and variance and to

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detect any sex differences or age × sex interactions. Regression estimates from each bootstrap sample were used to construct 95% confidence intervals around model-averaged fits.

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In order to estimate how our model-averaged fits would perform on new data, we used the .632+ bootstrap technique (Efron and Tibshirani, 1997). We termed these .632+ bootstrapcorrected coefficients of determination as population R2 to emphasize that they are different

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from adjusted R2 values commonly reported in aging literature.

In multi-model inference, statistical hypothesis testing is not common. Instead,

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researchers report model likelihoods and generally treat their results as exploratory in nature (Burnham and Anderson, 2002). To estimate statistical significance of our model-averaged regression fits, we combined randomization procedures with Monte Carlo simulations to estimate R2 distributions under the null hypothesis. Because the ratio of R2/(1-R2) is proportional to F-

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statistic scores, these null R2 distributions lead to the same p-value estimates as matching Fstatistic distributions under the null hypothesis.

First, we generated 25,000 non-repeating randomizations of volume data from each HC

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ROI. We used Akaike weights to compute model-averaged regression fits for data with no age or sex effects. In each of 25,000 simulations under the null hypothesis, we computed the proportion

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of variance explained by the regression model, as well the proportion of variance explained by age and sex effects. A separate set of Monte Carlo simulations was performed to estimate significance of age × sex interaction terms. We tested for overall regression significance first. Next we tested for significance of age, sex, and interaction effects. Last, we tested whether associations with age were statistically significant in each sex. We applied the Holm-Bonferroni procedure to correct for type I error

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inflation due to multiple hypothesis testing at the level of regression significance (1 test for total HC; 3 tests for total HC subregions; 3 tests for total HC subfields; 9 tests for HC subfields within each HC subregion). However, a second Holm-Bonferroni correction was applied when

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examining the relationships to age in each sex separately (2 tests for each HC ROI). We also compared model likelihoods for linear vs. non-linear fits. To control for overfitting, we report non-linearity likelihoods as the ratio of non-linearity likelihood in the HC ROI data over non-

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linearity likelihood in randomized data. Likelihoods ratios falling in the range between 6 and 10 provide moderate evidence in support of non-linearity. Likelihood ratios between 10 and 100

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provide strong evidence in support of non-linearity, while likelihood ratios above 100 provide essentially conclusive evidence that the relationship to age is non-linear. Next, we compared strengths of associations to age among HC ROIs. For such tests either theoretical framework (Lee and Preacher, 2013; Steiger, 1980) or simulation techniques can be

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used (e.g. bootstrap). Both methods produced similar Z-statistic estimates (r = .971, p < .001), which we subsequently averaged. To determine whether variance explained by age was different among the HC subregions and subfields we used these method-averaged Z-scores to perform

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two-tailed tests of significance with Holm-Bonferroni correction for multiple comparisons (3

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tests for total subregions; 3 tests for total subfields; 3 tests for subfields within each subregion; 3 tests for each subfield across subregions). Last, we examined how ICV normalization affected HC volumes. First, age-relationships

were removed from the ICV-adjusted data. Subsequently, age-corrected volumes were converted to age-corrected raw volume estimates using an inverse normalization procedure. Male and female raw HC subfield volume estimates were compared to each other using independent samples t-tests with the degree of freedom adjustment to account for the removal of age-related 13

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effects. Finally, similar to earlier hypothesis tests, Holm-Bonferroni correction for multiple comparisons was applied when testing for presence of sexual dimorphism in raw HC volumes (1 test for total HC; 3 tests for HC subregions; 3 tests for total HC subfields; 9 tests for HC

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subfields within each HC subregion).

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3. Results

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3.1. Demographics and descriptive statistics

Participant characteristics are shown in Table 2. Larger ICVs were found in males compared to females [males: µ = 1742 cm3, σ = 136 cm3; females: µ = 1520 cm3, σ = 111cm3; p < .001], while the two groups did not differ in age [males: µ=48.34, σ=19.4; females: µ=48.24, σ=17.7, p = .97] or education (p = .147). Absolute (raw) HC volumes are shown in Table 3. Asymmetry

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indices for total HC volumes, HC subregion volumes and HC subfield volumes did not differ between males and females [all ps > .27]. Years of education did not correlate with age in males

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or females [r = -.089, p = .293].

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3.2. Age and sex effects on the total HC and the antero-posterior HC subregion volumes

Total HC volume showed a non-linear relationship to age in both sexes [males: Population R2 = .0536, p < .007; females: Population R2 = .0284, p < .045], and this relationship was similar in males and females [interaction p > .215] (Fig. 2). Our analysis also revealed a significant main effect of sex in total HC volume [Population R2 = .0284, p < .001], demonstrating that relative to ICV, females had larger HC volumes than males [d = 0.545, Mdiff = 198.93 mm3 (≈ 5%)].

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Compared to similar data with no age or sex effects, the relationship between age and total HC volumes was 44.9 times more likely to be non-linear, indicating relatively high probability of non-linear association between age and the total HC volume.

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Next, we investigated how age and sex related to the HC subregion volumes. Regression models (Fig. 2) were statistically significant for the HC body [Population R2 = 0.301, p < .001], and HC tail [Population R2 = .0176, p < .009], while the HC head showed no age- or sex-related effects

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[p > .140].

Analyses of age-related effects demonstrated significant association with age in the HC body

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[Population R2 = 0.148, p < .001], but not the HC tail [p > .60]. Compared to similar data with no age effects, the HC body was 1369 times more likely to contain non-linear age-related effects, suggesting very high probability of non-linear association between age and the HC body volume. The main effect of sex was significant in the HC body [Population R2 = .168, p < .001] and HC

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tail [Population R2 = .0597, p < .012]. In both of these subregions females had larger ICVnormalized volumes than males [hippocampal body: d = 0.952, Mdiff = 133.89 mm3 (≈ 11%); HC tail: d = 0.546, Mdiff = 63.93 mm3 (≈ 11%)]. No age × sex interactions were detected in any

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of the HC subregions [all ps > .25], suggesting similarity of age relationships in males and females across all HC subregions.

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Lastly, we investigated whether relationships to age were statistically different among the HC subregions. The proportion of variance explained by age was greater in the HC body than in the HC head [Zdiff = 2.73, p < .013], and greater in the HC body than in the HC tail [Zdiff = 3.49, p < .002]. However, the relationships to age in the HC head and the HC tail were similar to each other [p > .37].

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3.3. Age and sex effects on total HC subfield volumes Our multi-model regressions were statistically significant for the total DG [Population R2 = .178,

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p < .001] and the total Sub [Population R2 = .172, p < .001] subfield volumes (Fig. 3). However, we did not observe any significant associations between age or sex and the total CA1-3 volume (p > .140).

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Analyses of age-related effects demonstrated a significant association with age in the total DG volume (p < 0.050), although that relationship was statistically significant only in females

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[males: p > .152; females: Population R2 = .025, p < .049]. Although strengths of age relationships were marginally different between males and females, this difference was not statistically significant (interaction p > .713).

Within the Sub subfield, on the other hand, the age × sex interaction was statistically significant

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(p < .033). The relationship between the Sub subfield volume and age was statistically significant in males [Population R2 = .179, p < .001], but not in females [Population R2 ≈ 0, p > .059], demonstrating that the Sub subfield had a stronger association with age in males than in females.

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Furthermore, support for non-linear age relationships was stronger in the Sub (odds 3526:1) than in the DG (odds 9:1).

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The main effect of sex was present in total DG volumes [Population R2 = .175, p < .001], but not in total Sub volumes [Population R2 ≈ 0, p > .063]. Females had larger ICV-adjusted total DG volumes than males [d = 0.975, Mdiff = 154.96 mm3 (≈ 12%)]. Finally, we investigated whether relationships to age were statistically different among the HC subfields. The proportion of variance explained by age was greater in the total Sub than in the total CA1-3 [Zdiff = 2.474, p < .041]. The relationships to age between the total Sub and the

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total DG, as well as between the total CA1-3 and the total DG were not statistically different [both ps > 0.20].

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3.4. Age and sex effects on the HC subfield volumes within the antero-posterior HC subregions

Within the HC head regression models were statistically significant for the DG volume

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[Population R2 = .1161, p < .002], and for the Sub volume [Population R2 = .0101, p < .014], but not for the CA1-3 volume (p > .70) (Fig. 4). However, the DG model was driven entirely by

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the main sex effect [Population R2 = .122, p < .002], as all age relationships, including age × sex interactions, were not statistically significant [all ps > .20]. Females had larger ICV-normalized DG volumes in the HC head than males [d = 0.770, Mdiff = 46.25 mm3 (≈ 12%)]. The Sub volume, on the other hand, showed no sex differences (p > .795), while age associations and age

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× sex interactions were statistically significant [age: p < .012; interaction: p < .048). Further analysis revealed that significant association between age and Sub volume in the HC head was present only in males [Population R2 = .0574, p < .004], but not in females (p > .35). Odds of

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non-linear relationship between the Sub volume and age were 9:1. Investigating whether strengths of relationships to age were statistically different among the HC subfield volumes

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within the HC head revealed that the proportion of variance explained by age was greater in the Sub than in the CA1-3 [Zdiff = 2.398 p < .050]. The relationships to age between the Sub and the DG, as well as between the CA 1-3 and the DG were not statistically different [both ps > 0.30]. Within the HC body, regression models were statistically significant for all HC subfields [CA13: Population R2 = .198, p < .001; DG: Population R2 = .214, p < .001; Sub: Population R2 = .201, p < .001]. Significant non-linear associations between age and HC volumes were found in

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all subfields within the HC body [CA1-3: Population R2 = .095, p < .001; DG: Population R2 = .95, p < .001; Sub: Population R2 = .053, p < .002] (Fig. 5), and were statistically significant in males and females separately in every subfield (all ps < 0.045). No age × sex interactions were

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observed in any subfield within the HC body (all ps > .50), suggesting that relationships between subfield volumes and age were similar for men and women in the HC body. We found that global volume differences between males and females were present in every HC body subfield

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[CA1-3: Population R2 = .110, p < .002; DG: Population R2 = .124, p < .002; Sub: Population R2 = .093, p < .003], where females had larger ICV-normalized volumes than males [CA1-3: d =

0.719, Mdiff = 32.56 mm3 (≈ 10%)].

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0.756, Mdiff = 40.72 mm3 (≈ 10%); DG: d = 0.820, Mdiff = 62.77 mm3 (≈ 11%); Sub: d =

Investigating whether strengths of relationships to age were statistically different among the HC subfields within the HC body showed that the proportions of variance explained by age were

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similar in all 3 subfields (all ps > .80). Odds of non-linear models for these age relationships were 8:1 for the CA1-3, 218:1 for the DG, and 6830:1 for the Sub. Within the HC tail, regression models were statistically significant for all subfields [all ps < .05]

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(Fig. 6). Investigating age effects for each subfield separately revealed that the CA1-3 and the DG subfields showed no statistically significant age relationships of any kind [all age and age ×

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sex interaction ps > .30]. However, age × sex interaction was significant in the Sub subfield (p < .022). In the HC tail the Sub subfield showed marginally significant relationship to age in females [Population R2 = .031, p < .080], but not in males (p > .24). Investigating regression components further revealed presence of statistically significant main sex effect in all three subfields [CA1-3: Population R2 = .039, p < .024; DG: Population R2 = .044, p < .023; Sub: Population R2 = .063, p < .009], where females had larger ICV-normalized

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volumes than males [CA1-3: d = 0.476, Mdiff = 18.71 mm3 (≈ 10%); DG: d = 0.479, Mdiff = 39.49 mm3 (≈ 11%); Sub: d = 0.579, Mdiff = 5.30 mm3 (≈ 12%)]. Odds of a non-linear relationship between the Sub subfield within the HC tail and age were

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approximately 8:1. Because age relationships with the HC tail subfield volumes were modest, strengths of relationships to age were statistically similar among all 3 subfields within the HC tail (all ps > .09).

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Next, we compared whether strengths of age relationships were statistically different for HC subfields across all 3 HC subregions. The associations between age and the CA1-3 volumes as

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well as between age and the DG volumes were stronger in the HC body than in the HC head [CA1-3: Zdiff = 2.71, p < .021; DG: Zdiff = 2.53, p < .024], and stronger in the HC body than in the HC tail [CA1-3: Zdiff = 2.15, p < .064; DG: Zdiff = 3.23, p < .004]. Strengths of age associations for the CA1-3 and the DG were similar in the HC head and in the HC tail (both ps >

> .74].

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.37). The Sub, however, showed similar vulnerability to aging in all three HC subregions [all ps

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3.5. Sex differences in absolute HC volumes

Our regression models were built on ICV-normalized data; however, we wanted to examine

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whether sex-related differences, similar to the ones observed in the normalized data, were present in absolute (raw) HC volume measurements. After removal of age relationships from the HC data, two-sample t-tests, with the degree of freedom adjustment for age-relationship removal, were used to compare raw HC volumes between males and females. Without correcting for ICV, males had larger HC volumes than females [t = 4.41, p < .002, d = 0.774, Mdiff = 324.64 mm3 (≈ 9%)]. Of the three antero-posterior HC subregions, only the HC head showed sex-related

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differences in volume [t = 4.41, p < .004, d = 0.817, Mdiff = 254.43 mm3 (≈ 13%)]. The HC body and the HC tail, on the other hand, showed no statistically significant sex differences [both ps > .45]. Among the total HC subfields males had larger absolute CA1-3 volumes and larger

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absolute Sub volumes [CA1-3: t = 4.67, p < .003, d = 0.817, Mdiff = 204.95 mm3 (≈ 12%); Sub: t = 4.69, p < .004, d = 0.821, Mdiff = 84.15 mm3 (≈ 10%)], while the absolute DG volumes were similar in both sexes (p > .60). Of the HC subfields restricted to specific HC subregions males

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had larger CA1-3 in the HC head [t = 4.22, p < .021, d = 0.737, Mdiff = 163.52 mm3 (≈ 15%)] and larger Sub in the HC head [t = 4.68, p < .011, d = 0.818, Mdiff = 62.47 mm3 (≈ 13%)]. After

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statistical removal of age effects, all subfields within the HC body and within the HC tail, together with the DG subfield within the HC head, showed no sex-related differences in absolute

4. Discussion

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volumes (all ps > .45).

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To our knowledge, this was the first study to examine effects of healthy aging on HC subfields and antero-posterior HC subregions across the entire HC formation. There were several main

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findings from this study. First, we found that the HC body was the only HC subregion that showed a significant relationship to age in both males and females, while the HC head and the HC tail volumes were not associated with age. Second, while the total Sub and the total DG volumes showed the strongest and the weakest associations with age respectively, the total CA13 volume showed no significant relationship to age. Third, we found that significant relationships to age for the CA1-3 and the DG subfields were present only in the HC body, while the Sub subfield showed associations with age in the entire HC formation. Furthermore, significant age × 20

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sex interactions were found for the total Sub, driven by interaction effects in the Sub of the HC head and the Sub of the HC tail. Lastly, we found that age-volume relationships for the total HC,

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its subfields and its antero-posterior subregions were of non-linear nature.

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4.1. Patterns of aging for the total HC volume and antero-posterior HC subregion volumes

Despite the fact that the majority of previous MRI studies in healthy aging measured the HC

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volume as a whole structure several studies examined antero-posterior HC subregion differences in healthy aging. The present findings on relative preservation of the anterior HC (HC head) with age in contrast to reduction in volume of the HC body are in agreement with our earlier work (Malykhin et al., 2008), and a number of studies from other research groups (Driscoll et al., 2003; Kalpouzos et al., 2007). After ICV adjustment, Driscoll et al. (2003) observed significant

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volumetric reductions in the posterior, but not the anterior HC. In a voxel-based morphometry study by Kalpouzos et al. (2007) there were smaller volume reductions in anterior medial temporal lobe, including the amygdala and the anterior HC, than in its more posterior regions.

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Furthermore, it was shown that anterior medial temporal volumes were relatively preserved in

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the elderly after accounting for global cerebral volume reduction with age (Good et al., 2001; Grieve et al., 2005).

In contrast, other studies (Hackert et al., 2002; Rajah et al., 2010; Pruessner et al., 2001) demonstrated more significant age-related volume differences in the anterior HC. Pruessner et al. (2001) observed that age differences in healthy young adults (<42 years) within the HC were predominantly localized to the HC head and the HC tail. Another study from the same research

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group (Rajah et al., 2010) reported age-associated volumetric differences (young vs. old) in the HC head and in the HC body, but not the HC tail. Hackert et al. (2002) found that volumes of all the HC subdivisions negatively correlated with

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age in older (>60 years) participants. In addition, it was found that associations between age and HC volumes were more pronounced in the posterior segment of the HC head.

In contrast to our earlier work (Malykhin et al., 2008), we did not observe any statistically

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significant associations between age and HC tail volume. Such discrepancies between our earlier work and volumetric studies of the HC performed by other groups might be driven either by

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sampling variations or by variability in image analysis techniques. For instance, in this study if we restrict our analysis to individuals 60 years of age and older, we observe a trend towards a negative association with age for HC tail volumes (p = 0.07), but not for HC head volumes. This suggests that volumetric atrophy might indeed be present in the HC tail, but only in individuals

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who are 60 years of age or older.

In addition to volumetric differences between younger versus older adults (Driscoll et al., 2003; Malykhin et al., 2007; Rajah et al., 2010), significant associations between age and HC volumes

2002).

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can independently exist in younger (Pruessner et al., 2001) and older individuals (Hackert et al.

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Since relationships between the HC volume and age might not be of linear nature it is critical to determine whether non-linear functions provide better explanations for age effects on HC subregion and subfield volumes. Furthermore, the non-linear pattern of aging in the HC may help to explain such a diverse set of results reported by different studies. In addition, results could vary significantly, depending on sample composition in each study (Allen et al., 2005).

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The same logic holds for inconsistent findings among studies interested in age differences within specific HC subfields. Our results are in agreement with several previous MRI studies, which found that the HC follows

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a non-linear rather than linear pattern in volume decline with age (Allen et al., 2005, Fjell et al., 2013; Raz et al., 2004; Walhowd et al., 2005). The current study not only provides evidence in support of non-linear associations between age and total HC volumes, but also suggests age

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relationships in all of the HC subregions and subfields might be best explained by non-linear models. Generally, HC ROIs with stronger age effects were most likely to be best explained by

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non-linear models. Previous work by Daugherty et al. (2015) and de Flores et al. (2015) also reported the presence of non-linear relationships to age in some of the HC subfields. Although the precise functional role of the antero-posterior HC subregions remains unknown, several patterns of their functional specialization have been proposed. Two patterns of functional

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organization appear to be superimposed on the HC long axis: gradual and discrete transitions (Strange et al., 2014). The general cognitive domains that have yielded the greatest differential activation between anterior and posterior HC include memory encoding vs retrieval, stimulus

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novelty vs familiarity, and auditory vs visual processing, pattern separation vs completion, sharpening vs integration (Poppenk et al. 2013; Ranganath & Ritchey, 2012; Small et al., 2002,

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Strange et al., 2014).

In the present study the global HC volume reduction was driven primarily by the HC body where all subfields showed non-linear relationships with age. Surprisingly, the relative volumes occupied by each HC subfield within the total HC and its antero-posterior subregions remained stable throughout the entire adult lifespan. This suggests that other factors (i.e. connectivity patterns of the HC body, functional specialization of the HC body, or relationship with other

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MTL structures such as parahippocampal or entorhinal cortices) rather than subfield composition might be responsible for the preferential vulnerability of the HC body to aging. Furthermore, the behavioural effects of healthy cognitive aging may be tied to poor performance on specific

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memory tasks that depend on the HC body, as opposed to other HC subregions.

In the current study we have not yet analyzed any cognitive data in order to support different

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specialization of the antero-posterior HC subregions and the HC subfields and how age-related differences in their volumes might relate to memory performance. However, our previous study

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(Travis et al., 2014) provided support to differential specializations in episodic memory of the HC subregions and subfields. However, it remains to be determined how these associations are affected in healthy aging.

4.2. Hippocampal subfield volumes and aging

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The results of this study suggest that in addition to an anterior-posterior gradient in volume reduction with age, the HC subfield volumes were not uniformly affected along the hippocampal

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axis. Both the total Sub and total DG volumes were associated with age while the total CA1-3 volume showed no statistically significant association with age. However, all subfields within the

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HC body demonstrated significant associations with age, suggesting that volumes of these subfields might be related to each other within the HC body. In contrast, the Sub subfield showed associations with age in the entire HC formation, suggesting that this subfield might undergo different structural adaptation with age compared to the DG and the CA1-3. Furthermore, significant age × sex interactions found for the Sub subfield suggest that sex-related factors might play an important role in this process.

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Several previous cross-sectional volumetric studies that employed manual delineation of the HC subfields on high-resolution T2-weighted MRI images found that the HC subfields are not uniformly affected in healthy aging (Table 2). However, earlier studies neither examined effects

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of age on the antero-posterior HC subregions separately nor analyzed the HC subfields within the subregions independently for the entire HC. The majority of previous studies examined associations between age and HC subfield volumes based on volumetric data from 3-5 MRI

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slices of the HC body (Daugherty et al., 2016; Mueller et al., 2007, 2008; Mueller and Weiner 2009; Shing et al., 2011; Raz et al., 2014). Few other studies extended the analysis towards the

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HC head and tail, and due to limitations in MRI methodology (e.g. 2-mm slice gap between slices & fewer slices acquired) the authors did not analyze the entire HC (de Flores et al., 2015; La Joie et al., 2010). In a recent study (Wisse et al., 2014) HC subfields were measured across the HC head, body and part of the tail, but the most posterior part of the tail was segmented as a

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whole structure, since the subfields fused together and could not be delineated reliably. Reliable separation of the HC head from the amygdala and separation of the HC body from the HC tail depends on MRI resolution and the orientation of image acquisition. Our coronal FSE

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slices were acquired perpendicular to the AC–PC line to enable reliable delineation of all three HC subregions, especially where the HC body transitions into the HC tail. Visualization of the

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crus of the fornix in full profile, a major landmark for reliable delineation of the HC tail (Boccardi et al., 2014, 2015; Maller et al. 2007; Malykhin et al., 2008; Pruessner et al. 2001), is usually better on MRI images acquired perpendicularly to the AC-PC line. On images acquired in the perpendicular orientation to the long axis of the HC, the fornix bundle can become fragmented and indistinguishable from the fimbria. Orientation of MRI image acquisition is very unlikely to have any effect on volumetric measurements of HC subfields within the HC body

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because subfields display relatively consistent anatomy throughout the entire HC body. However, orientation of image acquisition would most likely have a significant effect on how the HC subfields are seen and measured within the HC head and tail, where subfield anatomy

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changes noticeably on every 1-mm thick slice.

All previous studies of the HC subfields in healthy aging that employed 3T or 4T magnets used 2-mm thick MRI slices with in-plane resolution varying between 0.375 and 0.4 mm (Table 2).

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The main advantage of such an acquisition protocol is a shorter acquisition time, which is a considerable advantage when scanning older participants. However, visualization of the HC

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subfields across the entire HC depends on the slice thickness that also varies between studies from 0.7 mm to 2 mm. Although all of the aforementioned studies were able to visualize the HC subfields within the HC body, delineation of HC subfields within the entire HC head and HC tail was only possible on MRI images acquired with 1 mm (or less) thick slices. The primary reason

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for this slice thickness requirement is caused by HC subfield transitions, which occur on the submillimetric scale, and slices thicker than 1 mm might not allow reliable visualization of changes in SLM shape within the HC head and tail. Schematic illustrations of the boundaries of

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the HC subfields in most HC segmentation protocols (Yushkevich et al., 2015) were derived from the anatomical atlas of the human HC (Duvernoy, 2005) where the bicommissural plane

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(i.e., AC–PC) was used in both histological and MRI examinations. Consequently, MRI images acquired with a different slice orientation from the AC-PC alignment might not be directly comparable to the HC subfield schematics presented in the Duvernoy (2005) atlas. A recent postmortem study (Ding & Van Hoesen, 2015) reported detailed organization of the HC subfields within the HC head and the HC body, where histological images were obtained perpendicular to the long axis of the HC. Therefore, the results from that study have to be

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critically reviewed when identification of subfields is performed on MRI slices acquired perpendicular to the long axis of the HC. Previous studies that examined the HC subfield volumes within the HC body employed a

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protocol developed by Mueller et al. (2007) with some modifications to the CA1-Sub boundary (Shing et al., 2011; Raz et al., 2014; Daugherty et al., 2015). These studies reported negative effects of age on volumes of CA1-2 and CA3-DG, but not the Sub (Table 2).

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For instance, Mueller et al. (2007) observed a negative linear relationship between age and CA1 volume. In a subsequent study, they discovered negative associations between age and CA1 as

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well as CA3&DG subfield volumes (Mueller and Weiner, 2009). In addition, their analysis by decade revealed that both CA1 and CA3&DG volumes remained relatively stable up to the 5th– 6th decade, after which a continuous volume loss begins. Another research group (Shing et al., 2011; Raz et al., 2014), which employed a modified version of the segmentation protocol

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developed by Mueller et al. (2007) found linear atrophy in the CA1-2 area in healthy aging. However, the association between CA1-2 volume and age was linked to arterial hypertension (Shing et al., 2011).

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Despite the fact that the majority of studies investigated only linear associations, several recent studies employed non-linear modeling techniques to study these relationships (de Flores et al.,

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2015, Daugherty et al., 2015). A recent study (Daugherty et al., 2015), with a larger sample size, confirmed a negative linear relationship between age and CA1-2 volume, but also reported a non-linear (quadratic) relationship between age and CA3-DG and entorhinal cortex volume, but not Sub volume.

Our findings also showed that the CA1-3 and the DG subfields within the HC body are negatively associated with age. However, in the current study all significant relationships

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between subfield volumes and age were non-linear. In addition, we also found that the Sub volume in the HC body was negatively associated with age. Some of the observed differences between studies could have arisen as a consequence of variability in segmentation methodology.

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Despite the differences in the nomenclature between the current and the aforementioned studies (CA1-3 and DG/CA4 versus CA3-DG) the CA and DG subfield boundaries mainly overlapped. In contrast, our definition of the Sub was somewhat different: we excluded the presubiculum and

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parasubiculum from our Sub volume, while other studies have included those in their Sub volume estimates (Daugherty et al., 2015; Mueller et al., 2007; Raz et al., 2014; Shing et al.,

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2011). Furthermore, the border between CA1 and Sub in our protocol was more lateral compared to work from other groups. Indeed, variability in the CA1-Sub border is one of the major drives behind recent initiatives to develop a common protocol for HC subfield segmentation (Yushkevich et al., 2015).

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In other studies, the manual delineation of HC subfields extended towards more anterior areas in order to include measurements from the HC head and the more posterior parts of the HC body (La Joie et al., 2010, de Flores et al., 2015). Despite limitations discussed earlier, La Joie et al.

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(2010) reported a significant negative linear effect of age on the volume of the Sub with a relative preservation of CA1 and CA2/3/4/DG subfields. In their follow-up study de Flores et al.

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(2015) observed a linear decrease of the volume of the Sub, a nonlinear decrease of CA1 volume dropping at around 50 years, and no significant differences in the other subfields. Since de Flores et al. (2015) did not investigate age relationships for the HC subfields in each HC subregion separately, it is unclear whether volumetric differences as a function of age for the CA1 and Sub volumes were driven by equal atrophy in all antero-posterior HC subregions or by more focal sources of atrophy. In the present study we also found negative associations with age for the total

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Sub volume. However, in contrast to de Flores et al. (2015), we found that age had a non-linear association with the total Sub volume. Furthermore, we found no significant correlations with age for the total CA1-3 volumes, while de Flores et al. (2015) reported a nonlinear decrease in

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CA1 volume beginning in the 6th decade. In addition, de Flores et al. (2015) combined the CA2,3,4 and DG into a single ROI (called “OTHER” by the authors) making their results difficult to compare with the results from the present study because we combined CA1,2,3 into a

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single “CA1-3” ROI, and the DG with the CA4 into a separate “DG” ROI. Another possible reason for the difference in CA1-3 findings compared to some previous MRI studies is that the

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grouping of CA1 with CA2-3 subfields may have obscured the effect of aging on CA1 observed in other studies (Mueller et al., 2007; Mueller and Weiner, 2009; Wisse et al., 2014; de Flores et al., 2015).

Wisse et al. (2014) found a significant age-related volumetric decrease in the CA1, the DG&CA4

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subfields and total HC volume, while the CA2, CA3, and Sub did not show any age-related volumetric differences. Since only total subfield volumes were investigated, it remains unclear if these volumetric reductions in CA1 and DG&CA4 subfield were driven by one particular

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subregion or occurred throughout the entire HC formation. Here, we addressed this knowledge gap by analyzing age relationships in each HC subfield within each HC subregion.

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Despite the mounting evidence of specific effects of age on HC subfield volumes, it is still unclear which histological changes are responsible for this volumetric atrophy. Previous stereological studies have demonstrated minimal neuronal loss in cortical and HC regions in aging (Bussiere et al., 2003; Gazzaley et al., 1997; Hof et al., 2003; West et al., 1994). Instead, reduction in the complexity of dendrite arborization and dendritic length, and decrease in synaptic density, and not overt neuronal loss were evident (Scheibel et al., 1975; de Brabander et

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al., 1998; Masliah et al., 2006; Dickstein et al., 2007). These age-related changes in dendritic morphology were associated with impairments in cognitive functions (Hof and Morrison, 2004). However, several postmortem studies demonstrated that the effects of aging might be localized

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in specific HC subfields rather than in the entire HC structure (West, 1993; West et al., 1994, 2004). For instance, regionally specific neuronal loss in aging human HC was found in the Sub (52%) and hills of the DG (31%) but not in the CA1 or CA2-3 subfields (West, 1993).

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Furthermore, while the most distinctive Alzheimer’s disease related neuronal loss was seen in the CA1 region of the HC, in the normal ageing there was almost no neuronal loss in this region

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(West et al., 1994, 2004). The present findings that total Sub and DG volumes but not CA1-3 volume were affected by age are in agreement with these postmortem findings. These age-related losses of neurons in the hilus and the Sub could compromise the processing of information

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within the HC and the transmission of HC information to other cortical areas (West, 1993).

4.3. Sex differences in HC aging patterns

Our finding that males had significantly larger absolute (raw) total HC, HC head, total CA1-3

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and total Sub subfield volumes was not surprising given the fact that males generally have larger total brain and intracranial volumes (Ruigrok et al., 2014; Jancke., 2014; Allen et al., 2005). All

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of these differences in HC subregion and HC subfield absolute volumes were driven almost entirely by males having approximately 15% larger CA1-3 and Sub subfields specifically within the HC head.

Except for the total HC volume, every single volumetric difference between males and females in the absolute HC volumes disappeared after correction for the ICV, and all HC subregions and subfields with no statistical difference in raw volumes became statistically significant, with

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females displaying larger relative HC volumes in every subfield in the HC body and in the HC tail, as well as larger DG volumes in the HC head. Although the DG comprised approximately 33% of the ICV-corrected HC volumes, more than 75% of sexual dimorphism in the total HC

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volume after adjusting for ICV was explained by sex-related volume differences in the DG. Significant age × sex interactions were found only in the Sub subfield indicating that sex-related differences in the CA1-3 and the DG volumes were unrelated to age. Furthermore, no sex-related

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differences in age relationships were found in any subfield within the HC body, demonstrating that this subregion was experiencing similar age-related volume decline in both sexes.

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Our results are in agreement with a recent meta-analysis of structural MRI studies of the human HC, which reported that males have larger uncorrected (raw) HC volumes, as well as larger ICV, and total brain volumes (TBVs) (Tan et al., 2016). The authors found that in studies that corrected unilateral HC volumes for individual differences in ICV or TBV, this effect was

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reduced to a non-significant 0.6% larger HC volume in females. In contrast, when TBV or both TVB and ICV corrections were used for bilateral HC volumes, significantly larger HC volumes were found in females. Several other studies of brain sex differences (Filipek et al., 1994; Cahill,

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2006 (Goldstein 2001); Durston et al., 2001; Gur et al., 2010) also reported that the HC is proportionally larger in females compared to males.

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Fjell and colleagues (2009) found that in women the HC occupies a larger part of the total brain volume compared to males. However, these sex differences in HC volumes were opposite if different normalization methods were applied: ICV-corrected bilateral hippocampal volumes were larger in males, whereas TBV-corrected volumes were larger in females. In summary, observed sex differences in the HC volume are highly sensitive to the method used for brain size correction.

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Raz et al. (2004) found that men demonstrated larger HC volumes even after controlling for height. However, these differences were present only in younger participants (<46 years of age), with no sex-dependent variation in older counterparts. Interestingly, the HC showed a stronger

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negative linear relationship to age in men than in women (Raz et al., 2004). In the current study similar negative non-linear trends were observed in both males and females. Pruessner et al. (2001) found no overall sex differences in the HC volume in a sample of younger adults (18-42

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years). However, men showed a consistent decline in the HC volume between the third and fifth life decade, while women’s HC volumes in this age range remained constant. In contrast, some

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other previous studies that investigated sex differences in aging HC rates did not find any differences between males and females (Allen et al., 2005).

The present study extends previous research by showing that the Sub subfield might be susceptible to sex-specific age-related decline, while the relationship between age and CA1-3 or

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DG volumes were similar in both sexes. Unfortunately, we could not address directly possible origins of these sex effects and whether they were related to estrogen since no hormonal measurements were collected.

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Because our results showed that females had larger ICV-adjusted volumes in the posterior HC (i.e. body and tail), it is crucial to have a similar number of males and females volunteers across

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the lifespan when the relationships between age and HC subregion volumes are examined, especially if sex effects are not modelled explicitly. The development and validation of the HC subfield segmentation protocols are challenged by the limited availability of data linking MRI appearance to the microscopic HC anatomy, particularly in three dimensions (Adler et al., 2014). While the resolution achieved by postmortem MRI studies (0.064-0.2 mm in plane) is not comparable to in vivo studies, post-mortem MRI failed to

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provide the features necessary to define most HC subfield boundaries directly (Adler et al., 2014). Therefore, in the present study all macroscopically defined HC subfield areas are only

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approximations of the microscopically defined subfields.

5. Summary and conclusions

The current study confirmed differential vulnerability of the HC subfields and antero-posterior

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HC subregions to healthy cognitive aging. We found that all effects of age on HC volumes were non-linear and were mainly found in the HC body. We demonstrated that the total Sub and DG

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subfield volumes were both affected by age, while the total CA1-3 volume was not. For the CA1-3 and DG subfield volumes, we observed associations with age only in the HC body, while the Sub subfield showed associations with age in the entire HC formation. Subiculum volumes

Acknowledgements

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were more negatively related to age in men than in women.

Financial support for this study was provided by the Canadian Institutes of Health Research

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(CIHR) operating grant MOP115011 (NM) and CIHR doctoral award to SH. All authors reported

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no conflict of interest.

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Table 1 Summary of manual volumetric MRI studies of hippocampal (HC) subfields in healthy aging.

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Abbreviations: CA1-3: Cornu Ammonis; DG: dentate gyrus; HC: hippocampus; Sub: subiculum;

Table 2 Participants characteristics

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Abbreviations: MOCA : Montreal Cognitive Assessment Test; ICV: Intracranial Volume.

Table 3 Absolute (raw) HC subfield and antero-posterior HC subregion volumes, as well as their

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relative distribution in young, middle age and older adults.

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Figure Captions Figure 1 Segmentation of the HC subfields within antero-posterior HC subregions (a-g) is shown on T2-weighted FSE images with inverted contrast: Coronal views of the HC head (Fig.

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1b, e); HC body (Fig. 1c, f); HC tail (Fig. 1d, g).

Three-dimensional reconstruction of the HC subfields from a healthy volunteer: view from the top (h, i, j).

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Abbreviations: CA1-3: cornu ammonis (shown in red); DG: dentate gyrus (shown in blue); Sub: subiculum (shown in green); SLM: stratum lacunosum-moleculare.

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Figure 2 Regression plots showing the relationship between age and the ICV-adjusted total HC volumes as well as the relationships between age the ICV-adjusted antero-posterior HC subregion volumes.

Model-averaged estimates for males (blue) and females (red) are shown separately. Shaded areas

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represent the 95% bootstrap percentile confidence interval for each fit. Figure 3 Regression plots showing the relationship between age and the ICV-adjusted total HC subfield volumes.

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Figure 4 Regression plots showing the relationship between age and the ICV-adjusted HC subfield volumes within the HC head.

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Figure 5 Regression plots showing the relationship between age and the ICV-adjusted HC subfield volumes within the HC body. Figure 6 Regression plots showing the relationship between age and the ICV-adjusted HC subfield volumes within the HC tail.

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Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI. Neuroimage 84, 505-23.

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Subjects (M/F), age (years)

MRI resolution 3 mm , coverage

HC subfields

n of HC slices analyzed

Total HC volume

Findings

Daugherty et al. (2015)

3T

n=202, (36/64%), 8-82

0.4×0.4×2 6 cm (30 slices)

CA1-2, CA3-DG, Sub

3 (HC body)

N/A

Volume of CA1-2 (linear), CA3-DG (nonlinear), but not Sub, showed negative associations with age.

de Flores et al. (2015)

3T

n=98, (41/57), 19-84

0.375×0.375×2, interslice gap = 2 mm,13 slices

CA1, CA2/3/4/DG, Sub

13 (entire HC)

yes (sum of all subfields)

Significant negative effect of age on volumes of CA1 (quadratic) and Sub (linear), but not on CA2/3/4/DG.

La Joie et al. (2010)

3T

n=50, (19/31), 19-68

0.375×0.375×2 interslice gap = 2 mm,13 slices

CA1, CA2/3/4/DG, Sub

13 (entire HC)

yes (sum of all subfields)

Significant negative effect (linear) of age on volumes of Sub and total HC, but not on CA1 or CA2/3/4/DG.

Raz et al., (2014)

3T

n=80 (25/75%), 22-82

0.4×0.4×2 6 cm (30 slices)

CA1-2, CA3-DG, Sub

3 (HC body)

N/A

Volume of CA1-2 (linear), but not CA3-DG or Sub, showed negative association with age.

Shing et al. (2011)

3T

n=39, (26/13), 20-78

0.4×0.4×2 6 cm (30 slices)

CA1-2, CA3-4&DG, Sub

3 (HC body)

N/A

Smaller volumes of CA1–2 in older adults.

Mueller et al. (2007)

4T

n=42, (26/16), 21-85

0.4×0.5×2, 4.8 cm (24 slices)

CA1, CA2, CA3/4&DG, Sub

5 (HC body)

yes Significant negative effect of age on the CA1 (automated) (linear), and total HC volumes. Men had larger CA2 volumes than women.

Mueller & Weiner (2009)

4T

n=66, (35/31), 28-85

0.4×0.5×2, 4.8 cm (24 slices)

CA1, CA1-2 transition, CA3&DG, Sub

5 (HC body)

yes Significant negative effect of age on the CA1 and CA3/4&DG volumes. (FreeSurfer)

Current study

4.7T

n=129, (59/75) 18-85

0.52×0.68×1.0, 9 cm (90 slices)

CA1-3, CA4&DG (DG), SUB

41-50 (entire HC)

yes (sum of all subfields)

Significant negative effect (non-linear) of age on the total HC, HC body (all HC subfields), total Sub (see the results), and total DG (in females) volumes.

7T

n=29, (45/55%) 65-80

0.7×0.7×0.7, (whole brain)

CA1, CA2, CA3 DG&CA4, Sub

entire HC

yes (sum of all subfields)

Significant negative effect (linear) of age on the CA1, DG&CA4 and total HC volumes.

Wisse et al. (2014)

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MRI scanner

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Study

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Descriptive statistics Std. Deviation

Range

Age (years)

47.6

18.9

18-85

Sex (M/F)

59/70

Education (years)

15.86

Handedness (L/R)

13/116

MOCA

27.63

1.33

3 ICV (cm )

1622

165

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2.46

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Mean

10-23

26-30

1108-2083

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Young adults (18-39 years, n=51 )

Middle age (40-59 years, n=36)

Males (n=24)

Females (n=27)

Males (n=15)

3

2127.2 ± 212 (53%)

1830.6 ± 303.2 (51%)

2183.7 ± 329.1 (54%)

3

1276.6 ± 184.1 (32%)

1208.5 ± 177.7 (33%)

1263.6 ± 147.5 (31%)

581.3 ± 140.4 (15%)

584.1 ± 126.1 (16%)

3985.8 ± 481

3623.2 ± 412.23

1776.5 ± 140.7

1514.9 ± 126.1

3

1242.7 ± 300.2 (69%)

1034.8 ± 224.3 (66%)

3

362.7 ± 67.4 (20%)

342.6 ± 67 (22%)

Females (n=22)

1925.4 ± 236.9 (52%)

1872.9 ± 313.3 (53%)

1768.9 ± 260.7 (51%)

1226.7 ± 151.9 (33%)

1084.7 ± 205.5 (30%)

1105.9 ± 159.2 (32%)

3

HC tail, (mm ), (% of total HC)

599.3 ± 109.4 (15%)

579.6 ± 108.6 (15%)

587.1 ± 117.2 (17%)

599.4 ± 153.1 (17%)

3

Total HC volume, (mm )

4046.7 ± 403.3

3731.7 ± 224.1

3544.7 ± 452.9

3474.2 ± 343

3

ICV (cm )

1752.7 ± 134.6

1527.4 ± 96

1694.7 ± 126.4

1519.9 ± 109.3

HC body, (mm ), (% of total CA1-3) 3

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HC head, (mm ), (% of total CA1-3)

1309.7 ± 223.9 (70%)

1076.2 ± 162 (67%)

1108.9 ± 222.3 (69%)

1022.8 ± 211.1 (67%)

339.9 ± 49.2 (18%)

332.4 ± 64.5 (21%)

293.4 ± 69.6 (18%)

306.4 ± 67.2 (20%)

205.1 ± 43.1 (12%)

192.3 ± 41.4 (12%)

199.7 ± 43.1 (13%)

198.6 ± 58.4 (13%)

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Cornu Ammonis (CA1-3)

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HC body, (mm ), (% of total HC)

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HC head, (mm ), (% of total HC)

192.6 ± 44.1 (11%)

3

1797.9 ± 314 (45%)

1568.5 ± 248.5 (43%)

1854.7 ± 234.2 (46%)

1600.9 ± 188.2 (43%)

1601.9 ± 260.1 (45%)

1527.9 ± 238.3 (44%)

381 ± 77.4 (30%)

366.9 ± 61.1 (29%)

369.2 ± 53.6 (29%)

389.8 ± 75.4 (31%)

353.7 ± 64.3 (30%)

341.4 ± 48.1 (29%)

Dentate Gyrus (DG) 3

HC head, (mm ), (% of total DG)

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HC tail, (mm ), (% of total CA1-3) Total CA1-3, (mm ), (% of total HC)

191.1 ± 42.2 (12%)

Older adults (60-85 years, n=42) Males (20)

Total HC subregion

Females (n=21)

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HC subregion/Subfield

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Young adults (18-39 years, n=51 )

Middle age (40-59 years, n=36)

Females (n=27)

Males (n=15)

3

553.7 ± 98.1 (43%)

534.6 ± 97.2 (43%)

558.9 ± 71.6 (44%)

HC tail, (mm ), (% of total DG)

341.9 ± 104.6 (27%)

347.6 ± 84.7 (28%)

349.9 ± 74.2 (27%)

3

1276.9 ± 209.1 (32%)

1249.1 ± 177.4 (34%)

1278.1 ± 131.1 (32%)

3

503.5 ± 105.2 (55%)

428.9 ± 69.5 (53%)

3

360.2 ± 55.3 (40%)

331.3 ± 55.5 (41%)

46.8 ± 11.9 (5%)

45.5 ± 10.9 (6%)

910.5 ± 123.1 (23%)

805.7 ± 95.9 (23%)

3

Total DG, (mm ), (% of total HC)

3

HC tail, (mm ), (% of total Sub) 3

Total Sub, (mm ), (% of total HC)

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HC body, (mm ), (% of total Sub)

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HC head, (mm ), (% of total Sub)

Females (n=22)

534.8 ± 74.6 (42%)

470.6 ± 95.2 (40%)

471.9 ± 84.1 (41%)

338.5 ± 78.9 (27%)

347.2 ± 87.3 (30%)

355.6 ± 99.4 (30%)

1267.1 ± 142.4 (34%)

1171.5 ± 180.2 (33%)

1168.9 ± 165.8 (34%)

504.7 ± 181.8 (55%)

459.5 ± 76.9 (53%)

410.4 ± 85.6 (53%)

404.8 ± 68.4 (52%)

364.8 ± 64.6 (40%)

359.9 ± 61.9 (41%)

320.6 ± 68.4 (42%)

327.6 ± 58.3 (42%)

44.4 ± 9.5 (5%)

48.8 ± 10.8 (6%)

40.2 ± 9.4 (5%)

45.2 ± 10.4 (6%)

913.9 ± 129.6 (22%)

868.2 ± 109.2 (23%)

771.2 ± 135.7 (22%)

777.6 ± 95.2 (22%)

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Subiculum (Sub)

Older adults (60-85 years, n=42) Males (20)

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Females (n=21)

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HC subregion/Subfield

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Highlights

• Changes in the hippocampus with normal aging have been studied extensively.

• The effects of age on hippocampal volumes are nonlinear. • The effects of age are mainly found in the hippocampal body.

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• It is unknown how hippocampal subfields and subregions are affected by age.

• We found that global subiculum and dentate gyrus volumes were associated with age.

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• Subiculum volumes were more negatively related to age in men than in women.

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1. All authors reported no conflict of interest

(CIHR) operating grant MOP115011 to Dr. N.Malykhin.

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2. Financial support for this study was provided by the Canadian Institutes of Health Research

3. The data contained in the manuscript being submitted have not been previously published,

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consideration at Neurobiology of Aging.

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have not been submitted elsewhere and will not be submitted elsewhere while under

4. Written, informed consent was obtained from participants and the study was approved by the University of Alberta Health Research Ethics Board.

5. All authors have reviewed the contents of the manuscript being submitted, approved its

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contents and validated the accuracy of the data.