Hippocampal Sub-Regional Shape and Physical Activity in Older Adults

Abstract

Hippocampal atrophy is a hallmark of Alzheimer’s disease pathology, and a target biomarker region for testing intervention efficacy. Over the last few decades, a growing body of evidence from animal and human models suggests that physical activity (PA) is associated with structural benefits to the hippocampus in older adults. Very few human studies, however have explored hippocampal sub-regional specificity of PA; this is significant considering that sub-regions of the hippocampus are associated with distinct cognitive tasks and are differentially affected by disease pathology. This study used objective and self-reported measures of daily walking activity and exercise, and surface-based regional shape analysis using high-field hippocampal sub-regional partitions to explore sub-region specific hippocampal associations in a sample of nondemented, community-dwelling older adults at elevated sociodemographic risk for cognitive decline. Vertex-wise surface areas, which may be more sensitive than global volume measures, were calculated using shape diffeomorphometry, and PA was assessed using step activity monitors and PA questionnaires. We found that daily walking activity in a participant’s environment was associated in cross-section mainly with larger surface areas of the subiculum in women. Associations remained significant when controlling for self-reported exercise. Prior studies have found that PA related to exercise and aerobic fitness may be most closely associated with the anterior hippocampus, particularly the dentate gyrus of the hippocampus. These novel findings are the first, to our knowledge, in human models to suggest that PA related to navigation that may not reach the level of moderate-intensity exercise may be associated with specific sub-regions of the hippocampus. These findings underscore the importance of better understanding the independent and related biological mechanisms and pathways by which increasing exercise as well as non-exercise, lifestyle PA may influence structural brain health.

INTRODUCTION

Over the last two decades, a growing body of evidence suggests that physical activity (PA), in particular exercise and increased aerobic fitness, may be beneficial to brain health (Hillman et al., 2008; Ahlskog et al., 2011; Brown et al., 2013; Carlson and Varma, 2015). Given the lack of successful pharmacologic interventions for Alzheimer’s disease (AD) and other types of dementia (Daviglus et al., 2010; Blazer et al., 2015), an increasing number of recent clinical trials have explored the cognitive benefits of exercise-related interventions among older adults. While no major clinical trial has yet shown that exercise can reduce the incidence of AD, a number of trials have shown some success in targeting risk factors associated with AD (Lautenschlager et al., 2008; Williamson et al., 2009; Erickson et al., 2011), with some exceptions (e.g., Sink et al., 2015)). Additionally, non-exercise, lifestyle PA within complex environments may protect against cognitive decline and dementia, and may be more effective than single-modal activities (Carlson et al., 2008; Carlson et al., 2012; Voss et al., 2013).

Physical activity includes a broad range of activities that increase energy expenditure above a resting level (Howley, 2001; NHLBI, 2011), and can vary by intensity (i.e., low-intensity—vigorous-intensity) as well as context (i.e., varying environmental complexity). The type of PA may promote better brain health and cognition through different mechanisms; moderate-intensity exercise may lead to benefits through increased aerobic and cardiovascular fitness (Colcombe and Kramer, 2003; Erickson et al., 2011) while increased lower-intensity lifestyle activities may lead to benefits through pathways related to navigating socially and cognitively enriched environments (Carlson et al., 2012).

While PA, and exercise in particular, may positively impact a broad network of neural structures including the frontal lobes, anterior cingulate cortex, inferior-temporal lobe, and the parietal cortex (Hillman, Erickson et al. 2008, Thomas, Dennis et al. 2012), the majority of animal-based (Cotman and Berchtold, 2002; Cotman and Berchtold, 2007; Kempermann et al., 2010) and human-based evidence (Hillman et al., 2008; Bherer et al., 2013; Carlson and Varma, 2015) indicate that the hippocampus is particularly structurally and functionally sensitive to PA. Research using mice and other animal models has indicated that voluntary exercise is associated with increased neurogenesis (Kempermann et al., 2002; Pereira et al., 2007), angiogenesis, and blood flow (Bloor, 2005; Pereira et al., 2007), synaptic strength (Farmer et al., 2004; Cotman et al., 2007), and other neuronal benefits to the hippocampus. Evidence additionally indicates that PA within complex and enriched environments may add complementary benefits to the effects of PA on the hippocampus (van Praag et al., 2000). Neurobiological evidence in human models has generally followed animal evidence showing that exercise is associated with reduced hippocampal atrophy (Smith et al., 2014) increased hippocampal volume (Erickson et al., 2011) and increased hippocampal perfusion including blood volume and flow (Pereira et al., 2007; Maass et al., 2015). These findings are promising particularly because hippocampal atrophy is an anatomical hallmark of the progression of AD, and serves as a key biomarker for the diagnosis of early and presymptomatic AD (Cummings, 2009; Jack et al., 2013) as well as a target for interventions (McEwen, 1997; Erickson et al., 2011; Carlson et al., 2015; Carlson et al., 2016).

While the hippocampus is a target biomarker region for understanding intervention effects and neuropathology, subregions within the hippocampus perform distinct cognitive and computational tasks and may be differentially affected by aging, AD, and PA interventions (Small et al., 2011). These structurally and functionally different sub-regions include the dentate gyrus (DG), cornu ammonis region 1 (CA1), CA region 2 (CA2), CA region 3 (CA3), and the subiculum. Evidence mainly from animal models indicates that voluntary exercise as well as environmental enrichment may lead to regionally specific increased cell proliferation and neurogenesis in the DG sub-region (van Praag et al., 1999; Faherty et al., 2003; Eadie et al., 2005; Sahay et al., 2011; Dery et al., 2013), the only subregion of the hippocampus where neurogenesis occurs. Some animal studies have also found evidence for synaptic remodeling in the CA3 and CA1 subregions, in addition to the DG subregion, after wheel running (Schaefers et al., 2010), and increased dendritic spine density in the CA1 subregion after environmental enrichment (Faherty et al., 2003) and voluntary running (Stranahan et al., 2007).

Very few human studies have explored the relationship between PA and the hippocampus with sub-regional specificity. Some studies using specialized techniques to segment the hippocampus into sub-regions have found that aerobic exercise may primarily affect the anterior hippocampus, including the DG, CA1, and subiculum, with weaker effects on the posterior hippocampus (Erickson et al., 2011; Maass et al., 2015; Thomas et al., 2015). Additionally, cerebral perfusion associated with increased aerobic activity may affect all subregions (Maass et al., 2015) or may be specific to the DG subregion (Pereira et al., 2007). Understanding the region-specific associations with specific types of PA is critical to a better conceptual understanding of the biologic mechanisms and associated time course by which increased PA may positively impact brain health. Furthermore, understanding these specific associations will enable clinical trials of PA to better select target regions to explore intervention effects.

This study used objective and self-reported measures of daily walking activity and exercise, and surface-based regional shape analysis using high-field hippocampal subregional partitions to explore subregion specific hippocampal associations with PA in a nondemented, community-dwelling older adult sample at elevated sociodemographic risk for cognitive decline.

METHODS

Study Sample

Participants were from the Brain Health Study (BHS), a substudy within the Baltimore Experience Corps Trial (BECT), a sex-stratified, randomized, controlled effectiveness trial to explore the health benefits of volunteer service. Details on sex-stratification, randomization, descriptions of intervention and control conditions, recruitment and other study design details have been described previously (Fried et al., 2013). Enrollment criteria for the larger BECT included 1) age ≥ 60 year; 2) a score of ≥ 24 on the Mini-Mental State Exam (MMSE) (Folstein et al., 1975); and an ability to read at a minimum sixth-grade level based on the Wide Range Achievement Test [WRAT-4; (Wilkinson, 1993)]. Additional enrollment criteria for the BHS has been described previously (Carlson et al., 2015), and included (1) right hand dominance; (2) free of a pacemaker or other ferrous metals in the body; and (3) no history of brain cancer or brain aneurism/stroke in the past year.

A total of 123 participants were enrolled in the BHS at baseline. For this study, 12 participants were excluded because they did not complete magnetic resonance imaging (MRI) protocol due to excessive head movement or claustrophobia. An additional 21 did not complete or did not provide usable PA data based on exclusion criteria (described later) resulting in a final sample of 90 participants. All measurements in this baseline, cross-sectional study occurred prior to intervention or control placement. Participants in the final study sample did not vary significantly (P < 0.05) from those in the remaining BECT sample on any socio-demographic characteristic other than sex; the BHS over-sampled men by design in order to allow for sex-stratified comparisons (Kuo et al., 2015). The study protocol was approved by the Johns Hopkins School of Medicine Institutional Review Board, and each participant provided written informed consent.

Walking Activity Measure

The majority of evidence for the relationship between PA and cognition has focused on exercise measured by self-reported as well objective measures of fitness (e.g., VO2 max, FEV) [e.g., (Colcombe and Kramer, 2003; Angevaren et al., 2008; Erickson et al., 2011)]. Recently, body-worn accelerometers have become a popular method to objectively measure total PA (Bassett et al., 2014, Troiano et al., 2014). Objective total PA measures incorporate a variety of activities including exercise (e.g., aerobic exercise) and non-exercise activities, including lifestyle activities (e.g., walking for pleasure) and instrumental activities of daily life (e.g., housework). Broadly, these measures indicate the amount of total movement—specific to body placement—within an individual’s environment.

In this study, total PA was measured using an ankle worn accelerometer, developed to specifically measure total walking activity. The step activity monitor (SAM; Orthocare Innovations, Mt. Terrace, WA) is a pager-sized microprocessor-linked accelerometer that measures number of steps in one-minute intervals using acceleration, position, and timing information. The SAM has been validated across a range of community dwelling older adult samples with varying functional limitations using self-reported and objective measures (e.g., hand-tallied step counts and other accelerometers) (Resnick et al., 2001; Cavanaugh et al., 2007; Storti et al., 2008). The SAM is particularly sensitive to activity at decreased gait speeds (Storti et al., 2008), and is well tolerated by older adults (Algase et al., 2003).

Detailed wear and cleaning protocols have been described previously (Varma et al., 2014). Briefly, participants were instructed to wear the SAM for 3–7 days while keeping a wear-time diary used to determine compliance. Participants were instructed to remove the SAM only when bathing, showering, or swimming. Data cleaning protocol was designed based on a study population that was sedentary at baseline with a high number of chronic conditions, and included the exclusion of days that represented noncompliance based on both a data driven compliance algorithm that defined wear and nonwear time during waking hours as well as participants’ self-report of noncompliance in their diaries. Participants provided an average of 3 days of data (SD: 1.47; range: 1–9).

We defined total walking activity as the number of steps/day completed by participants. We additionally segmented total steps/day into steps at low-intensity (>0 steps/min and <100 steps/min) and at moderate- to vigorous-intensity (≥100 steps/min). Intensity ranges, or effort associated with walking, were based on studies translating laboratory measurements of oxygen consumption while walking into pedometer-based metrics (Tudor-Locke et al., 2005; Marshall et al., 2009).

Exercise Measure

Exercise was assessed using the Community Health Activity Model Program for Seniors (CHAMPS), a well-validated questionnaire developed to sensitively measure variability in PA among older adults (Stewart et al., 2001). The CHAMPS questionnaire was administered to all participants during their baseline assessment. Participants were asked whether they participated in a range of PA in the past 4 weeks; for activities that participants engaged in, frequency and total hours of participation were assessed. On the basis of a formula utilizing activity-specific metabolic equivalent (MET) values developed by the American College of Sports Medicine (Stewart et al., 2001), we calculated total caloric expenditure/week for each activity. To calculate a measure for exercise, we summed total caloric expenditure/week for all exercise-related PA (e.g., jogging or running; walking leisurely or fast for exercise/pleasure; riding a bicycle or stationary cycle). All measures were administered by a trained evaluator.

MRI Acquisition and Hippocampus Segmentation

The participants underwent MRI scans using a 3.0 T Phillips scanner (Best, the Netherlands). The imaging protocol was: high-resolution T1-weighted 3D-volume Magnetization Prepared Rapid Gradient Echo Imaging (sagittal acquisition; repetition time = 8.037 ms; echo time = 3.7 ms; flip angle = 8°; field of view = 200 mm × 256 mm × 200 mm; matrix size = 256 mm × 256 mm; and voxel size = 1 mm × 1 mm × 1 mm).

The bilateral hippocampi were obtained using a fully automated segmentation pipeline (Tang et al., 2013, 2015) which is based upon a multi-atlas approach. This pipeline has been proven to be capable of delivering automated results with a segmentation accuracy of 92% for the hippocampus in each hemisphere for this BHS dataset. Details on selecting the multiple atlases, manually delineating the atlases’ hippocampal labels, and validating the segmentation performance for these data can be found in a forthcoming study (Tang et al., 2016). Briefly, we used 16 atlases (a subset of the BHS sample) with the left and right hippocampus manually delineated. The accuracy of this hierarchical pipeline in segmenting the bilateral hippocampi was evaluated using a leave-one-out strategy; one atlas image was treated as the to-be-segmented image and the remainder served as the atlas set to segment that excluded image.

Shape Processing

In recent years, advancements in neuroimaging and associated analytic techniques have allowed researchers to develop novel ways of characterizing hippocampal morphometry that may be more sensitive to age-related effects than traditional global volume measures (Voineskos et al., 2015). Changes in the shape and surface area of the hippocampus may be particularly important in understanding disease progression in Alzheimer’s (Carmichael et al., 2012; Tondelli et al., 2012), and provide a more nuanced and multi-dimensional (via vertices) representation of hippocampal subregions rather than a single number volumetric measure (Voineskos et al., 2015).

In this study, the hippocampal shapes for both hemispheres were represented by 2D triangulated surfaces contouring the boundary of the 3D structural segmentations. To generate these surfaces, we employed an approach that has been validated on a variety of MRI datasets [e.g., (Miller et al., 2015; Tang et al., 2015a,b)]. Details of this approach have been detailed previously (Tang et al., 2014). After generation of the surfaces, to quantify the vertex-wise surface areas of each participant’s shape, the focus of our correlation analyses, we relied on diffeomorphometry wherein a diffeomorphism is used to quantify the localized shape morphometrics. The participant-specific diffeomorphisms, each connecting a common template shape to an individual participant shape, were created using Large Deformation Diffeomorphic Metric Mapping for surfaces (Vaillant and Glaunes, 2005). The “common” or average template surfaces of the bilateral hippocampi were generated from the collection of surfaces from the total sample (n = 90) of this study using a Bayesian estimation algorithm [detailed in (Ma et al., 2010)] to ensure “sample-averaging”.

A scalar field was then calculated from each participant-specific diffeomorphism; the log-determinant of the Jacobian of the diffeomorphism, which is indexed by the vertices of the common template surface. In this way, the vertex-wise surface morphometrics of all participants are indexed in a common space, allowing for inter-participant analyses. Mathematically, the log-determinant of the diffeomorphism’s Jacobian quantifies the factor by which the diffeomorphism expands or shrinks the localized surface area in the participant relative to the common template surface in a logarithmic scale. In other words, a positive value corresponds to a localized surface area expansion relative to the common template while a negative value corresponds to a localized surface area contraction. This scalar field (surface area expansion or contraction) is referred to as the deformation marker; this study focused on the correlation of this marker with PA.

Template Surface Partition

To identify the region-specific associations, we partitioned our bilateral hippocampal template surface into four anatomical sub-regions (CA1, CA2, CA3 combined with the dentate gyrus, and the subiculum) using the approach detailed in (Tang et al., 2014). Briefly, this partition procedure was accomplished by manually partitioning the hippocampal surfaces of a high-field MR scan (obtained from a 7 Tesla scanner with an image voxel resolution of 0.8 mm) and transferring the boundary definitions of those sub-regions to our template surfaces using the LDDMM transfer technique.

Covariates

To control for potential confounders, we included covariates associated with both PA and brain volume, including intracranial volume (ICV), age, years of education, body mass index (BMI), cardiovascular disease burden (CVD), and global cognitive function. ICV was calculated as the sum of grey matter, white matter, and cerebrospinal fluid using FreeSurfer software and protocol (Chuang et al., 2013; Carlson et al., 2015); BMI was calculated using height and weight; CVD burden was calculated by summing participants’ self-report of hypertension, diabetes, heart attack/myocardial infarction, intermittent claudication, congestive heart failure, and angina/chest pain due to heart disease; and global cognitive function was assessed using the MMSE. All covariates were administered by trained evaluators and were calculated at baseline.

Analysis

The values of the shape deformation marker at each voxel of the bilateral hippocampi were examined for correlations with PA measures (including objective walking activity and self-reported exercise), with the correlation strength quantified by the Pearson correlation coefficient (PCC). The statistical significance of the correlation is measured by a P value obtained from nonparametric permutation tests (40,000 permutations were employed in this study). Because correlations were performed at each vertex, we corrected for multiple comparisons by using two standard approaches; controlling the family-wise error rate (FWER) (Nichols and Hayasaka, 2003) and the false discovery rate (FDR) at a level of 0.05 (Benjamini and Hochberg, 1995).

For our primary analyses, we explored shape correlation separately for total walking activity (steps/day) and total caloric expenditure/week in exercise-related PA. For our secondary and more exploratory analysis, we explored whether nonexercise walking activity may be correlated with shape of the hippocampi. To target this activity, we excluded moderate- to vigorous-intensity walking activity and explored shape correlations specific to low-intensity walking activity while covarying for self-reported exercise. This methodology was similar to that used previously to explore associations between hippocampal volume and nonexercise walking (Varma et al., 2014).

In all correlational analyses, we included all covariates described previously. Additionally, in keeping with our previous study (Varma et al., 2014), we performed all analyses separately for women and men; the BHS was designed specifically to explore sex differences (Fried et al., 2013).

RESULTS

Table 1 presents baseline demographic, health and PA characteristics of the sex-stratified study sample. Study participants averaged 67.3 years, 70.0% were women, 89.0% were African-American, 38.2% had a high school level of education or lower, and on average had an MMSE score of 28.4. Women and men did not vary significantly (P ≥ 0.05) on any sociodemographic characteristic at baseline.

TABLE 1.

Baseline Characteristics of the Brain Health Substudy Sample (N = 90)

		N (%) or Mean ± SD
Demographic
Age (years)		67.3 ± 6.0
Sex (women)		63 (70.0)
Race (African American)		80 (89.0)
Education (≤ high school)		34 (38.2)
MMSE		28.4 (1.5)
	Women		Men
Chronic Disease
Obesity (BMI ≥ 30)	41 (65.1)		11 (40.7)a
Hypertension	47 (75.8)		17 (64.0)
Diabetes	18 (29.0)		9 (33.3)
Physical activity
Objective walking activity
Steps/day (total)	7519.1 ± 2814.5		8755.2 ± 3496.1
Steps/day at low−intensityb	6875.7 ± 2454.1		7737.0 ± 2454.1
Steps/day at moderate− to vigorous-intensityc	643.5 ± 802.0		1018.2 ± 1472.8
Self-reported exercise
Total caloric expenditure/weekd	1595.2 ± 1593.8		2124.1 ± 2365.7

SD = standard deviation; MMSE = Mini Mental State Exam.

^aP < 0.05; t test/chi2 for differences by sex; women and men did not vary significantly on any demographic characteristic.

^bLow-intensity defined as walking activity at <100 steps/min.

^cModerate- to vigorous-intensity is defined as walking activity at ≥100 steps/min.

^dAssessed using the self-reported exercise-related physical activities from the Community Health Activities Model Program for Seniors (CHAMPS) questionnaire.

Participants were at high risk for cognitive and physical function decline due to high rates of chronic disease; a significantly greater percentage of women were obese compared to men (65.1% vs. 40.7%), and 75.8% and 29.0% of women reported hypertension and diabetes respectively. Sixty-four percent and 33% of men reported hypertension and diabetes respectively. On average, women completed a total of 7519.1 (SD: 2814.5) steps/day, with 6875.7 (SD: 2454.1) steps at low-intensity and 643.5 (SD: 802.0) steps at moderate- to vigorous-intensity. Women additionally expended 1595.2 (SD: 1593.8) calories/week in exercise related activity. Generally, women had marginally lower PA across all measures compared to men. On average, men completed a total of 8755.2 (SD: 3496.1) steps/day, with 7737.0 (SD: 2454.1) steps at low-intensity and 1018.2 (SD: 1472.8) steps at moderate- to vigorous-intensity. Men also expended 2124.1 (SD: 2365.7) calories/week in exercise related activity. Women in a larger sample (Varma et al., 2014), within which this BHS study is nested, had significantly (P < 0.05) lower PA than men. We additionally explored associations between total daily walking activity and self-reported exercise on the CHAMPS (Fig. 1). In women, walking activity was not correlated with exercise (Pearson’s correlation: r = 0.04; P = 0.78; Spearman’s correlation: ρ = 0.07; P = 0.61) while in men both metrics were correlated (Pearson’s correlation: r = 0.51; P = 0.01; Spearman’s correlation: ρ = 0.33; P = 0.10), suggesting that walking activity in women may reflect nonexercise activity.

FIGURE 1.

In women, average total steps/day was not significantly correlated with self-reported exercise (Pearson’s correlation: r = 0.04; P = 0.78; Spearman’s correlation: ρ = 0.07; P = 0.61); in men average total steps/day was correlated with self-reported exercise (Pearson’s correlation: r = 0.51; P = 0.01; Spearman’s correlation: ρ = 0.33; P = 0.10).

The maps of correlation between PA and the vertex-wise diffeomorphometrics, or shape, for superior and inferior views of the bilateral hippocampi (left and right hippocampus) are included in Figure 2; correlations are displayed only in regions where shape vertices correlated statistically significantly with PA after performing multiple comparison correction by controlling the FWER at the level of 0.05. Panel (a) includes the significant results from the primary analyses: vertex-wise PCC values obtained when correlating total walking activity with shape diffeomorphometry in women for the left and right hippocampus. Panel (b) includes significant results from the exploratory analysis describing values from correlating low-intensity walking activity, when controlling for self-reported exercise, with shape diffeomorphometry in women for the left hippocampus (no significant results detected for the right hippocampus). Finally, Panel (c) provides a reference for the subdivisions of the template surface into four subregions: CA1, CA2, CA3 combined with the DG, and the subiculum for superior and inferior views of the left and right hippocampus. We included exploratory analyses only when significant associations were indicated in the primary analyses.

FIGURE 2.

All panels indicate superior and inferior views of the bilateral hippocampi (left and right hippocampi) where correlations are statistically significant. Panel (a) indicates bi-lateral spatial maps of correlation between hippocampal shape diffeomorphometry and total walking activity in women. Panel (b) indicates bi-lateral spatial maps of correlation between hippocampal shape diffeomorphometry and low-intensity walking activity after co-varying for self-reported exercise in women. Panel (c) is a reference column indicating bi-lateral subdivisions of the template surfaces into four compatible subregions: CA1, CA2, CA3 combined with the dentate gyrus, and the subiculum. Correlations in women with self-report exercise did not reach statistical significance thresholds and therefore were not included; correlations between all physical activity measures and hippocampal shape diffeomorphometry in men did not reach statistical significance thresholds and therefore were not included. Note: The color bar denotes the Pearson product-moment correlation coefficient (PCC) values, which were which were plotted only for vertices whose correlations were statistically significant after a multiple comparison correction.

For women, significant positive correlations were found between total walking activity and hippocampal shape (Panel (a)) on selected vertices mainly belonging to the inferior rostral subiculum and secondarily to the superior rostral CA2 (immediately adjacent to the subiculum) of the left hippocampus (total surface area: 13.47 mm²) and the caudal subiculum (inferior) of the right hippocampus (total surface area: 15.38 mm²). No significant correlations were detected between self-reported exercise and bilateral hippocampal shape. Significant positive correlations were found between low-intensity walking activity, after partialing out the effect of self-reported exercise, and left-hippocampal shape (Panel (b)); significant vertices (total surface area: 39.96 mm²) included part of the inferior rostral subiculum and the superior part of the rostral CA1. For men, no significant correlations after the FWER correction were found between PA, including total walking activity and self-reported exercise, and bilateral hippocampal shape.

While low-intensity walking activity was significantly correlated with moderate- to vigorous-intensity walking activity in men (r = 0.79; P < 0.01) and women (r = 0.32; P = 0.01), moderate- to vigorous-intensity walking activity was not significantly correlated with hippocampal shape in men or women.

DISCUSSION

In this study, we explored the associations between PA and shape of sub-regions of the bilateral hippocampi within a sample of community-dwelling older adults at sociodemographic risk for functional and cognitive declines. Using shape diffeomorphometry to explore sub-regional associations that are more specific than global volume associations used in the majority of prior studies, we found in women and not men that objectively measured walking activity was cross-sectionally associated mainly with larger surface areas of the subiculum in the hippocampus. These novel findings suggest that PA that does not reach the level of moderate-intensity exercise within an environment may be associated with specific subregions of the hippocampus which are not typically associated with exercise and aerobic fitness. This underscores the importance of better understanding the independent and related biological mechanisms and pathways by which increasing exercise as well as nonexercise PA in daily life may influence structural brain health.

Prior human studies, albeit limited, exploring the relationship between PA and subregional structural volumes of the hippocampus have focused on PA related to exercise and aerobic or cardiorespiratory fitness [e.g., (Erickson et al., 2009)]. These studies indicate that increased exercise may be associated with benefits to the anterior hippocampus, a region that includes the DG, CA1, and subiculum. Animal studies focusing on neurogenesis have mainly indicated that PA primarily affects the DG subregion (van Praag et al., 1999; Faherty et al., 2003; Eadie et al., 2005; Sahay et al., 2011; Dery et al., 2013). The effects of low-intensity daily PA that may be unrelated to exercise or aerobic fitness, on the hippocampus as well as its subregions, are only beginning to be explored using sensitive, real-world measurement techniques (Voss et al., 2014; Burzynska et al., 2015; Voss et al., 2015). Our novel, regionally specific shape findings can be interpreted through an understanding of both the function of the subiculum as well as the type of PA measure used in this study. The dorsal hippocampus, which includes the subiculum and CA1 subregions, are located at the end of the trisynaptic circuit (DG → CA3 → CA1 → Subiculum); this region is the primary source of output projections from the hippocampus to other brain regions (de la Prida et al., 2006; O’Mara et al., 2009; Fanselow and Dong, 2010). There has been evidence indicating that projections from this region to other cortical regions, including the ventral tegmental area, play a critical role in locomotion (Swanson and Kalivas, 2000). Functionally, the subiculum provides a cognitive map for navigation and is involved primarily in the mediation of cognitive processing related to navigation and exploration, as well as learning and memory (Taube, 2007; Fanselow and Dong, 2010).

Physical activity measures related to real-world navigation within open environments may be associated with the neurobiology of the subiculum (O’Mara, 2005). In this study, we used an accelerometer to objectively measure daily walking activity within a community-living cohort. While other studies exploring the brain structural benefits from PA have focused primarily on aerobic exercise and fitness, including aerobic capacity [e.g., (Erickson et al., 2009; Erickson et al., 2011)], the measure used in this study was designed to precisely quantify daily walking activity, which may be considered a measure of locomotion and navigation within an environment. Women in this cohort were generally sedentary (Varma et al., 2014) with very low levels of exercise. Additionally walking activity in women, compared to men, was not correlated with self-reported exercise. While there may be a sex difference in self-report bias (Prince et al., 2008), the lack of correlation suggests that walking activity among women in our sample may be related to nonexercise activity and unrelated to fitness. Furthermore our exploratory analyses (excluding moderate- to vigorous-intensity walking activity and covarying for exercise) that indicated consistent hippocampal subregional specificity in women, suggests that low-intensity walking activities that require locomotion and navigation, including lifestyle activities and independent activities of daily living, may be specifically associated with the subiculum subregion of the hippocampus. Prior work from our group indicates that PA of participants within this study sample occur in a variety of environments within and outside the home, and greater PA, even at lower-intensity ranges, is associated with environmental navigation and complexity (Carlson, 2011; Carlson et al., 2012, 2016). Future longitudinal investigation within lifestyle interventions, such as the Experience Corps program from which this sample was obtained, will inform understanding as to whether modest increases in PA may have subregion specific effects on the hippocampus, particularly in the subiculum.

Consistent with results from previous work (Varma et al., 2014), our significant findings were sex-specific; we found no significant shape correlations with any PA measures in men. Prior studies indicate a sex difference in biological responses to PA, including walking and exercise; the effect of PA on cognition may be more pronounced in women than men (Colcombe and Kramer, 2003; Day, 2008; Hamer and Chida, 2009; Brown et al., 2013). Sex hormones may partially explain this difference; older women may have lower levels of neuroprotective sex hormones (Pike et al., 2009) compared to men and may be more sensitive to differences in PA (Brown et al., 2013). Finally, women in many study samples have lower PA levels and are at a higher risk of developing cognitive impairment than men, which may influence associations (Brown et al., 2013). It is important to note that in our study sample, women generally had greater chronic disease burden and lower levels of walking activity and exercise compared to men.

While we generally found bilateral symmetry in effects across analyses, we found left hippocampus-specific correlations with low-intensity walking activity and no correlation with the right hippocampus in women. These lateralized results may have been driven partially by a conservative statistical approach to controlling the FWER. When we decreased the P value threshold, we found significant bilateral correlations.

This study has limitations. First, although the cross-sectional design of the study allows us to explore correlations; we cannot come to a causal conclusion about the effect of walking activity on sub-regions of the hippocampus. While the inclusion of a global cognitive test (MMSE) as a covariate may partially account for reverse causation, we cannot discount this possibility. Second, while this study represented a unique, and understudied at-risk segment of the older adult population, the sex-stratified study sample was relatively small. The lack of significant findings in men in particular must be considered with caution. Finally, while our analytic models allowed us to speculate about the associations between nonexercise walking activity and brain structure, the SAM does not discriminate among types of activity.

Using novel shape diffeomorphometry based methods and sensitive measures of walking activity; this study is, to the best of our knowledge, one of the first to find sub-regional associations between PA and shape of the subiculum of the hippocampus. Considering that subicular atrophy is one of the earliest signs of AD pathogenesis in the hippocampus (Apostolova et al., 2006; Carlesimo et al., 2015), this study’s findings encourage us to better understand the underlying biological mechanisms that may explain how the brain can benefit from even modest increases in daily activity.