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. Author manuscript; available in PMC: 2022 May 2.
Published in final edited form as: Res Aging. 2021 May 12;44(2):205–214. doi: 10.1177/01640275211017268

The Role of Race in Relations of Social Support to Hippocampal Volumes Among Older Adults

Desirée C Bygrave 1, Constance S Gerassimakis 2, Denée T Mwendwa 3, Guray Erus 4, Christos Davatzikos 4, Regina S Wright 2
PMCID: PMC9060640  NIHMSID: NIHMS1791130  PMID: 33977830

Abstract

Evidence suggests social support may buffer brain pathology. However, neither its association with hippocampal volume, a marker of Alzheimer’s disease risk, nor the role of race in this association has been fully investigated. Multiple regression analyses examined relations of total social support to magnetic resonance imaging-assessed gray matter (GM) hippocampal volumes in the total sample (n = 165; mean age = 68.48 year), and in race-stratified models of African American and White older adults, adjusting for select covariates. Results showed greater social support was associated with greater GM hippocampal volumes among African American older adults only (p < .01). Our findings suggest greater total social support may play a role in supporting the hippocampus, particularly among African American older adults, who had lower hippocampal volumes than their White counterparts. Further research is needed to test these questions longitudinally and examine which aspects of social support may promote hippocampal integrity, specifically.

Keywords: social support, hippocampus, gray matter, race

Hippocampal atrophy, a key characteristic of early Alzheimer’s disease (AD) progression (Schröder & Pantel, 2016) is associated with increased concentrations of neurofibrillary tangles and amyloid plaque deposition, neuronal loss (Blanken et al., 2017; Goukasian et al., 2019) and reduced tissue volume (Salat et al., 2011). It also predates clinically evident cognitive dysfunction, particularly within executive function (Evans et al., 2018) and memory domains (Goukasian et al., 2019). Within a normal cognitive aging trajectory, global and hemispheric declines in hippocampal volume are normative and common findings on magnetic resonance imaging (MRI; Kurth et al., 2017), with one meta-analysis estimating an average yearly hippocampal atrophy rate of 1.4% among adults between mean aged 69 and 83 years (Barnes et al., 2009). Other evidence suggests that older adults with greater hippocampal volume may remain dementia-free, even in the presence of AD pathology (Erten-Lyons et al., 2009; Zolochevska & Taglialatela, 2016). Considerable loss in hippocampal volume, however, can signal early cerebrovascular pathology and increased risk for dementia in the non-clinical older population (Apostolova et al., 2008), even among those with higher estimates of cognitive reserve (Whalley et al., 2004; Zolochevska & Taglialatela, 2016).

Social Support and Hippocampal Volume

Evidence suggests that positive psychosocial functioning, which can be conceptualized as helpful and protective constructs that support positive affect and well-being, may slow the development of brain pathology in older adults (Desai et al., 2010). Findings from social neuroscience purport that the characteristics of one’s social network could promote prosocial behaviors that significantly preserve brain structure and function or induce plasticity-related changes in the brain, in part, by modulating the adverse effects of psychological stress (Davidson & McEwen, 2012). The hippocampus may be uniquely sensitive, such that the rate of hippocampal atrophy and subsequent cognitive decline among older adults may be slowed under optimal psychosocial conditions (Fotuhi et al., 2012). Evidence also suggests the hippocampus is involved in a range of social processes (Montagrin et al., 2018). One such characteristic of the social environment is perceived social support. Social support is a construct used to characterize one’s social network and is defined as “a social network’s provision of psychological and material resources intended to benefit an individual’s ability to cope with stress” (Cohen, 2004, p. 676).

Social support has been shown to mitigate the effects of physiological stress on the older adult brain (Cohen, 2004; Hostinar & Gunnar, 2015; Sherman et al., 2016), with some of the greatest effects detected within multiple limbic forebrain structures, including the hippocampus (Cohen & Willis, 1985; Hostinar & Gunnar, 2015; Sherman et al., 2016). Mechanistically, studies have linked the hippocampus with inhibition of hypothalamo–pituitary–adrenocortical (HPA) axis activity, and subsequent physiological damage due to increasing HPA axis activity (Ulrich-Lai & Herman, 2009). Such findings implicate the hippocampus in regulating the termination of stress-initiated HPA responses.

The Role of Race in Hippocampal Volume and Social Support Relations

There is a striking absence of studies that have examined the potential salutary influence of social support on hippocampal volume in older adults or examined this association in the context of non-modifiable characteristics such as race. Research shows that African Americans have a disproportionate risk for dementia and cerebrovascular disease in late life (Carmichael & Newton, 2019; Steenland et al., 2016), are 64% more likely to develop AD compared to their White counterparts (Steenland et al., 2016), and display poorer MRI-based indices of brain health compared to their White counterparts over time (Carmichael & Newton, 2019). Moreover, older African Americans display greater brain atrophy, which is partially attributable to a higher prevalence of vascular disease (Carmichael & Newton, 2019; Carnethon et al., 2017; Steenland et al., 2016), known to promote oxidative stress and neuroinflammatory responses which can trigger amyloid production or increase brain amyloid levels (Steenland et al., 2016).

Underlying racial disparities in dementia are unique stressors faced by African American older adults that promote cognitive decline and poor brain health (Turner et al., 2017). Previous research has suggested that African Americans experience a disproportionate burden of stressful life experiences linked to poverty, racial discrimination, and residential segregation, due, in part, to their relatively low social status in U.S. society (Clark et al., 1999; Paradies, 2006). Racial discrimination, in particular, has been associated with many adverse health outcomes (Williams et al., 2019); the effects of racial discrimination over time play an important role in cognitive health and exacerbating dementia disparities (Barnes et al., 2012; Glymour & Manly, 2008; Johnson et al., 2020). Importantly, the hippocampus has a high concentration of glucocorticoid receptors that are the primary binding sites for cortisol; cortisol is elevated in the context of chronic stress and has been linked to reduced hippocampal volumes (Zimmerman et al., 2016).

With respect to differences in social support and hippocampal volume relations, we are not aware of any published evidence; however, there is evidence of racial difference in social support networks and interaction. For example, while White older adults tend to have more frequent social interactions, African American older adults tend to have significantly more kin support, in part, shaped by factors such as extended household composition (Peek & O’Neill, 2001; Taylor et al., 2016), and religious involvement (Chatters et al., 2009; Peek & O’Neill, 2001).

Focus on Gray Matter (GM) Hippocampal Volume

Recent work examining the neural substrates of social support in older adults showed that the gray matter (GM) networks associated with perceived overall and tangible social support were composed of brain regions, including the hippocampus, that are linked to cognitive aging and dementia (Cotton et al., 2020). In normative cognitive aging, GM volume loss, rather than White matter shrinkage, is the principal cause of total brain volume reduction (Whalley et al., 2004), has a linear age-related pattern of decline (Ge et al., 2002), and a known association with incident cognitive decline/preclinical AD (Zanchi et al., 2017). Similarly, GM hippocampal atrophy often occurs within cognitive impairment that does not meet AD criteria, suggesting its potential sensitivity to early stages of AD (Apostolova et al., 2008; Whalley et al., 2004). For these reasons, a focus on GM hippocampal volume is an important piece of this inquiry, particularly as we examine older adults who may possess higher levels of cognitive reserve.

In sum, our review of the literature highlighted reduced hippocampal volume as an important characteristic of both normative aging and AD among older adults and discussed the potential mitigating role of social support. Examining the association between social support and GM hippocampal volume, as well as the role of race, may enhance our understanding of the beneficial role of social support for brain health, and any racial differences in derived benefits. It was hypothesized that greater social support would be associated with greater GM hippocampal volume, particularly among African Americans, who may experience more chronic stress and have unique social support ties.

Method

Data Source

We conducted a cross-sectional study of community-dwelling non-Hispanic White and African American adults ages 60 and older that aimed to examine relations of subclinical CVD to brain pathology and cognitive function among older adults. Participants were recruited from xx (blinded for review) and surrounding areas via the local newspaper, recruitment posters, newspaper ads, email, recruiting events, and word-of-mouth. Prospective participants were pre-screened by phone then invited for a formal in-person screening.

Study exclusion criteria included a history of clinical CVD, renal disease, hepatic disease, chronic pulmonary disease, hematological disease, neurological disease, HIV/AIDS diagnosis, chemotherapy, or radiation treatment within the past year. Study criteria also excluded participants with a history of type 1 diabetes or uncontrolled type 2 diabetes (HbA1c > 7), severe hypertension (systolic blood pressure [systolic BP] ≥ 180 mmHg or diastolic blood pressure [diastolic BP] ≥ 110 mmHg), use of anticoagulants, history of a severe head injury (loss of consciousness > 30 min), history of a severe psychiatric disorder, and heavy alcohol use (> 14 drinks/week). Participants were pre-screened for MRI contraindications (e.g., metal in eye, pacemaker, aneurysm clip, incompatible metallic prostheses) and were excluded from the study if they were present. Finally, participants were excluded if they were moderately or severely depressed with a Beck Depression Inventory-2 (BDI-2) score > 24 (Beck et al., 1996), if there was probable cognitive impairment with a Mini-Mental Status Examination (MMSE) score < 24 (Folstein et al., 1975), or if they attained less than 8 years of education.

The study consisted of two in-person visits within a 14-day range. Visit 1 included a full health screening performed by a nurse practitioner. Next, depression and cognitive impairment screenings were performed by a trained researcher. The 21-item BDI-2 assessed depressive symptoms (Beck et al., 1996) and the MMSE (Folstein et al., 1975) assessed for cognitive impairment. Next, participants deemed eligible to continue completed a series of psychosocial and health behavior measures and a demographic questionnaire. Also, during Visit 1, participants completed a neuropsychological test battery that assessed cognitive function in domains of attention, executive function, and memory. During Visit 2, to assess glucose and lipids, a fasting blood draw was collected by a research nurse and analyzed at Quest Diagnostics (Philadelphia, PA). Additionally, three seated and three supine blood pressure readings were taken, and average values calculated. Finally, vascular imaging was utilized to assess subclinical indices, then MRI was utilized to assess global and hemispheric brain volumes. Participants received a $50 gift card at the completion of each visit. The study was approved by the Institutional Review Board of the University of Delaware. Only measures included in the scope of this study were analyzed and discussed.

Measures

Assessment of perceived social support.

The 12-item Interpersonal Support Evaluation List (ISEL-12) is an abbreviated form of the 40-item ISEL and consists of 12 items with four response options ranging from “definitely false” to “definitely true.” The ISEL-12 assesses the perceived availability of certain types of social support: appraisal, belonging, and tangible support (Cohen et al., 1985). The appraisal subscale measures the perceived availability of someone to talk about one’s problems and includes items such as “When I need suggestions on how to deal with a personal problem, I know someone I can turn to.” The belonging subscale measures the perceived availability of people one can do things with. It includes items such as “If I decide one afternoon that I would like to go to a movie that evening, I could easily find someone to go with me.” Lastly, the tangible subscale measures the perceived availability of material aid and includes questions such as “If I were sick, I could easily find someone to help me with my daily chores” (Cohen et al., 1985). Each subscale consists of four items and is summed with scores ranging from 0 to 12. The sum of the three subscales indicates an individual’s total social support. Possible summed scores for total social support range from 0 to 36. Higher summed scores represent greater social support. Cronbach’s α across all 12 items was .83 in this sample. The OMEGA macro for SPSS was used to calculate the Mcdonald’s ω coefficient (Hayes & Coutts, 2020). Mcdonald’s ω (maximum likelihood) across all 12 items was .83 in this sample.

Hippocampal volume.

Volumetric characterization of the hippocampus was examined by MRI assessments of GM (Doshi et al., 2016). Evidence shows GM volume generally decreases with advancing age (Ge et al., 2002; Hafkemeijer et al., 2014).

MRI Acquisition

Participants underwent structural MRI in a Siemens 3T Prisma MRI scanner at xx (blinded for review) for Biomedical and Brain Imaging. MRI acquisition was overseen by a MRI technologist. Contraindications for MRI were assessed by the MRI technologist using a standard questionnaire and interview. A quick localizer and four sequences were acquired with a total acquisition time of approximately 18 min. High-resolution T1-weighted anatomical scan of each subject was first preprocessed for correction of intensity inhomogeneities. A 3D MPRage sagittal T1-weighted scan (TR = 1,900, TE = 2.93 ms, flip angle = 9°, inversion time = 900 ms, 1 mm slice thickness, 176 mm slices, 256 × 256 mm2 matrix, FOV = 250 mm); 3D sagittal FLAIR (FL; TR = 6,000, TE= 289 ms, TI = 200, 1 mm slice thickness, 160 mm slices, 202 × 256 mm2 matrix, FOV = 258 mm) and 2D sagittal T2 space sequence (TR = 3,200, TE = 48 ms, 1 mm slice thickness, 176 mm slices, 256 × 256 mm2 base matrix) were then run consecutively.

MRI analysis.

Analysis of all MRI data was conducted at the xx (blinded for review) for Biomedical Image Computing and Analytics using a well-defined systematic quality control protocol, composed of computerized and manual steps, and detailed in Doshi et al. (2016). A region of interest (ROI) label was assigned to each voxel in a method which obtained top accuracy in independent evaluations when compared against other benchmark methods (Asman et al., 2013; Doshi et al., 2016). MUlti-atlas region Segmentation utilizing Ensembles (MUSE) methodology used automated segmentation of anatomical structures for the delineation of each ROI (Doshi et al., 2016). This multiatlas skull stripping algorithm was applied for the removal of extra-cranial material with individual components (Doshi et al., 2013). GM, white matter, the cerebrospinal fluid, and ventricle volumes were ROI classified. Hemispheric frontal, temporal, occipital, parietal, and deep brain volumes were calculated within each ROI, as well as in larger anatomical regions obtained by grouping single ROIs within a hierarchical representation. Total, right hemisphere (RH), and left hemisphere (LH) GM hippocampal volumes were calculated and utilized in the analysis.

Covariates

Increased chronological age (Ge et al., 2002), fewer years of formal education (Rzezak et al., 2015), elevated BP (Gianaros et al., 2006), depressive symptoms (Zhou et al., 2016), and the unique effects of male and female sex (Xu et al., 2000), have documented links to baseline global and hemispheric brain and GM atrophy and were analyzed for their association with hippocampal volume using model subset selection. Age, sex, average supine systolic BP (mmHg), hypertension medication use, and depressive symptoms were adjusted for in the final analysis as they were significantly correlated with the outcomes examined.

Statistical Analysis

Due to the innovation of the study, there was not published information for precise sample size calculations. We used G*Power 3.1 (UCLA IDRE) to determine approximate sample size, a priori. Assuming 1 independent variable (total social support) and 6 covariates, an α value of .05, and an R2 value of .25 among the covariates, we determined that 65 subjects in each racial group would provide 80% power to detect a R2 value of .05 for the independent variable. Regression analyses were performed using SPSS version 25.0. Variables were tested for normality where necessary and descriptive statistics were run. Participant characteristics stratified by race were compared using chi-square or Fisher’s exact tests for categorical variables and independent-samples t tests for continuous variables. Multiple linear regression analyses were run to assess the association between total social support and GM hippocampal volumes. We regressed the three outcome variables (total, RH, and LH GM hippocampal volumes) on total support in separate models, controlling for age, sex, systolic BP, hypertension medication use, and depressive symptoms. Finally, following analytic methods utilized by Dawson et al. (2015) and Crews et al. (2010), race-stratified linear regressions were run to test associations within race, adjusting for covariates. Lastly, we applied the Bonferroni correction to determine an appropriate cut-off for significance. We divided p = .05 by 3, the number of outcomes regressed on total social support, to get the Bonferroni critical value of p < .0167 for significance.

Results

Sample Characteristics

The final sample consisted of 165 older adults with a mean age of 68.48 years (SD × 6.27). Among them, 33% were men, 41% self-reported as African American, 59% were married, 79% reported having graduated high school or higher, 10% reported having graduated college, graduate school, or professional training, 44% reported a hypertension diagnosis, 43% reported taking hypertension medications, and 76% reported an annual household income that covered their financial needs “well” or “very well.” On average, participants were cognitively normal (based on the mean MMSE score), with minimal depressive symptoms (based on the mean BDI-2 score), were overweight, and had normal total cholesterol values, elevated average supine systolic BP (>130 mmHg), and normal average supine diastolic BP (<80 mmHg).

No racial differences in age, educational attainment, or marital status emerged in the analysis. With respect to cardiometabolic health, African Americans were more likely to report a hypertension diagnosis, a type 2 diabetes diagnosis, a high cholesterol diagnosis, and the use of hypertension medication than their White counterparts (p < .05). There were no significant racial differences in depression or total social support scores; however, there were significant differences in hippocampal volumes. African Americans had lower RH, t(147) = 3.38, p < .001, LH t(147) = 3.22, p < .001) and total, t(147) = 3.44, p < .001) GM hippocampal volumes. Total and stratified sample characteristics are reported in Table 1.

Table 1.

Sample Demographic Characteristics.

% or Mean ± Standard Deviation
 p Value
Overall African American White
Continuous Variables
 Age 68.48 ± 6.27 68.22 ± 5.16 68.66 ± 6.94 .660
 ISEL-12 social support (total) 29.30 ± 5.72 29.75 ± 5.14 29.08 ± 6.00 .566
 Total GM hippocampal volume (ml) 7.24 ± .76 6.98 ± .76 7.40 ± .71 .001
 RH GM hippocampal volume (ml) 3.74 ± .43 3.59 ± .44 3.83 ± .40 .001
 LH GM hippocampal volume (ml) 3.50 ± .35 3.38 ± .35 3.57 ± .34 .002
 Mini-Mental Status Examination (total score) 29.08 ± 1.07 28.90 ± 1.12 29.21 ± 1.02 .059
 BDI-2 (total score) 4.15 ± 4.16 4.79 ± 3.24 3.70 ± 4.65 .099
 Body mass index (kg/m2) 29.64 ± 6.21 31.60 ± 6.93 28.26 ± 5.28 .011
 Average seated systolic BP (mmHg) 137.56 ± 19.38 141.46 ± 19.42 134.98 ± 19.00 .036
 Average seated diastolic BP (mmHg) 75.10 ± 9.94 77.42 ± 10.62 73.57 ± 9.21 .015
 Average supine systolic BP (mmHg) 134.33 ± 17.97 140.20 ± 18.74 130.44 ± 16.40 .001
 Average supine diastolic BP (mmHg) 72.31 ± 8.47 74.33 ± 9.03 70.98 ± 7.84 .013
 Total cholesterol (mg/dl) 190.64 ± 33.56 179.94 ± 32.25 197.93 ± 32.61 .001
Categorical variables
 Sex (% Male) 33 28 37 .314
 Race (% African American) 41 - - -
 Level of education (%) .101
  Less than high school 21 27 16
  High school graduate 25 22 28
  Some college or specialized training 44 43 44
  College or Professional graduate 10 8 12
 Marital Status (% Married) 59 67 53 .292
 High cholesterol diagnosis (%) 38 46 33 .049
 Hypertension diagnosis (%) 44 64 31 .000
 Diabetes diagnosis (%) 7 12 3 .026
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Note. n = 165.

*

p values are for African American versus White comparisons.

ISEL-12 = Interpersonal Support Evaluation List-12; RH = right hemisphere; LH = left hemisphere; GM = gray matter; BDI-2 = Beck Depression Inventory-2.

Regression Findings

In the models examining the total sample that were adjusted for age, sex, systolic BP, hypertension medication use, and depressive symptoms, no significant associations were found between total social support and total, RH, and LH GM hippocampal volumes. However, the adjusted race-stratified models revealed significant results for African American older adults. In the African American subsample, total social support was significantly associated with RH GM hippocampal volume (β = .49, p = .018), LH GM hippocampal volume (β = .58, p < .001) and total GM hippocampal volume (β = .56, p < .001). In each case, greater total social support was associated with greater GM hippocampal volume (Table 2). No significant associations were found for White older adults (Table 3).

Table 2.

Multiple Linear Regression Analysis: Gray Matter Hippocampal Volume Regressed on ISEL-12 Total Support Score for African American Older Adults.

Total Hippocampus β SE B β Adj. R2 F p
Model 1 .021 1.628 .213
ISEL-12 .032 .025 .234
Model 2 .301 3.079 .023
ISEL-12 .076 .024 .557**
Age .002 .024 .017
Sex .547 .270 .351
systolic bp −.023 .009 −.488*
Hypertension medication .424 .277 .272
BDI-2 (total score) .058 .038 .257
RH Hippocampus
β SE B β Adj. R2 F p
Model 1 .016 1.466 .236
ISEL-12 .019 .016 .223
Model 2 .173 2.014 .105
ISEL-12 .042 .016 .489*
Age .007 .016 .076
Sex .200 .184 .204
Systolic bp −.015 .006 −.514*
Hypertension medication .240 .189 .246
BDI-2 (total score) .028 .026 .197
LH Hippocampus
β SE B β Adj. R2 F p
Model 1 .014 1.412 .245
ISEL-12 .013 .011 .219
Model 2 .440 4.796 .003
ISEL-12 .034 .009 .582**
Age −.004 .009 −.070
Sex .348 .104 .517**
Systolic bp −.008 .003 −.385*
Hypertension medication .184 .107 .273
BDI-2 (total score) .030 .015 .308
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Note. ISEL-12 = Interpersonal Support Evaluation List-12; bp = blood pressure; BDI-2 = Beck Depression Inventory-2; RH = Right hemisphere; LH = Left hemisphere.

**

p < .01.

*

p < .05.

Table 3.

Multiple Linear Regression Analysis: Gray Matter Hippocampal Volume Regressed on ISEL-12 Total Support Score for White Older Adults.

Total Hippocampus β SE B β Adj. R2 F p
Model 1 −.006 .593 .444
ISEL-12 .010 .013 .092
Model 2 .184 3.637 .004
ISEL-12 .012 .013 .114
Age −.022 .012 −.248
Sex .354 .155 .258*
systolic bp −.004 .006 −.105
Hypertension medication −.246 .168 −.171
BDI-2 (total score) −.011 .018 −.072
RH Hippocampus
β SE B β Adj. R2 F p
Model 1 .006 1.407 .240
ISEL-12 .008 .007 .141
Model 2 .129 2.731 .020
ISEL-12 .009 .007 .147
Age −.008 .007 −.165
Sex .192 .088 .253*
systolic bp −.002 .003 −.087
Hypertension medication −.118 .096 −.148
BDI-2 (total score) −.010 .010 −.123
LH Hippocampus
β SE B β Adj. R2 F p
Model 1 −.014 .056 .813
ISEL-12 .002 .006 .029
Model 2 .222 4.329 .001
ISEL-12 .003 .006 .066
Age −.014 .006 −.318*
Sex .162 .075 .239*
systolic bp −.002 .003 −.116
Hypertension medication −.129 .081 −.181
BDI-2 (total score) −.001 .009 −.009
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Note. ISEL-12 = Interpersonal Support Evaluation List-12; bp = blood pressure; BDI-2 = Beck Depression Inventory-2; RH = Right hemisphere; LH = Left hemisphere.

**

p < .01.

*

p < .05.

Discussion

In this cross-sectional study, we examined overall and within-group relations of total social support to MRI-assessed GM hippocampal volume in a community-dwelling sample of White and African American older adults. Results revealed greater total social support was associated with greater total, RH, and LH GM hippocampal volumes among African American older adults, but not the total sample or White older adults. These results suggest that race moderates the association between social support and hippocampal volume, potentially reflecting a greater benefit for African Americans.

Our findings for African Americans align with the ‘social hippocampus’ hypothesis. This hypothesis suggests optimal hippocampal structure, activity, function, and associated memory networks are related to adaptive social processing and prosocial behaviors (Montagrin et al., 2018). In our study, the receipt of social support may operate as a prosocial behavior that promotes hippocampal structure. These distinct findings for African Americans only, however, beg the question – what is unique about social support in this population? African American culture is collectivist in nature and the community tends to play a prominent role in the African American family (Kim & Mckenry, 1998). According to classic articles on African American social support, this racial/ethnic group tends to rely on a social support network that includes the extended family, community, fictive kin, and church members (Brown, 2008; Hatchett & Jackson, 1993; Taylor & Chatters, 1988). African American church attendance, in particular, is strongly correlated with larger social networks and greater perceived support (Lee & Sharpe, 2007). We did not measure these factors directly; however, to the extent that African Americans in our sample utilize these rich sources of support, it may play a significant role in preserving hippocampal volume. Notably, many of our African American participants were recruited through church health ministries. Future studies should examine social support in a more nuanced way to determine whether it is the type/component of support or the quantity of overall support that confers benefits that influence and promote the integrity of the hippocampus.

Our findings for African Americans also align with evidence from the social neuroscience literature that suggests pro-social behaviors help to preserve brain structure and function by modulating the effects of stress (Cohen, 2004; Davidson & McEwen, 2012). Since African American older adults are more likely to report higher stress levels (Geronimus et al., 2006) and are 64% more likely to develop AD compared to their White counterparts (Steenland et al., 2016), examination of how social support may impact unique psychological stressors and brain health in this population is critical. Older African Americans experience cumulative stress associated with being an older racial minority (Clark et al., 1999), including social inequalities and racial discrimination throughout the life course that accelerate biological aging (Levine & Crimmins, 2014; Lewis et al., 2015). These life course stressors may cause older African Americans to be more vulnerable to current, late-life stressors, as evidenced by higher allostatic load as compared with Whites (Geronimus et al., 2006). These types of stressors may be differentially affected by social support and are consistently shown to contribute to health disparities (Lewis et al., 2015). Evidence supports that social support is a particularly salient buffer against the unique stressors experienced by African Americans (Dolezsar et al., 2014; Lewis et al., 2015; Mustillo et al., 2004). Following this assertion, the resources provided by social support may ultimately enhance hippocampal integrity via physiological and psychological stress reduction, as evidenced in previous studies (Cohen, 2004; Hostinar & Gunnar, 2015; Sherman et al., 2016).

Contrary to findings for African American older adults in the study, social support among White older adults did not appear to confer the same benefit for GM hippocampal volume, despite no significant difference in total social support scores between groups. Independently of our conclusions that African American older adults may derive more benefit due to their collectivist social interactions and unique stressors that may benefit more from the receipt of social support, we posit two other plausible explanations for differences in findings. One possible explanation for non-significant findings among Whites is that social support may provide greater benefit for those with lesser hippocampal volume. That is, among African Americans whose hippocampal volumes were significantly lower than Whites, social support may play a more vital role as a buffer against hippocampal atrophy. Second, given that the hippocampus is vulnerable to cardiovascular and cerebrovascular disease processes (Fotuhi et al., 2012), and that African Americans in our sample were more likely to self-report related risk factors, it is possible that social support may act as a buffer with respect to hippocampal atrophy, in the context of poorer cardiovascular and cerebrovascular health. However, longitudinal research is needed to confirm these ideas.

Overall, this study had several strengths. First, our study included African American older adults, a group still largely underrepresented and understudied in aging research. The inclusion of African Americans allowed us to explore the influence of social support on brain health in a vulnerable population of older adults, thereby adding to the literature that suggests positive social networks and interactions may foster a reduction in health disparities. Second, our inclusion of MRI data was a major strength, particularly with respect to African American older adults that are largely absent from MRI studies. Lastly, we examined hippocampal volume in cognitively normal older adults, adding to the literature examining pre-clinical correlates of brain health. Despite these strengths, we acknowledge several limitations. First, the cross-sectional design of the study precluded us from examining how changes in total social support over time influence changes in hippocampal volume over time. The limited sample size prohibited examination of higher order analyses (i.e., interactions of race with additional sociodemographic factors) that may further inform our understanding of the nature of the association between social support and GM hippocampal volume. Also, the use of a self-reported measure of social support may have produced a response bias at the time of administration. Lastly, our sample was highly educated and predominately female, thus caution should be taken when generalizing to the populations of African American and White older adults.

Conclusion

In conclusion, our study suggests that greater social support may help to buffer against GM hippocampal volume loss among African American but not White older adults. Future studies can propel this line of research by recruiting diverse samples of older adults, examining the potential mediating role of stress-induced HPA responses, examining the qualitative aspects of social support that may promote hippocampal integrity and overall brain health, and approaching all of these inquiries longitudinally. In addition, further investigation of social support and macrostructural and microstructural brain structures can help to identify additional patterns of brain pathology that may benefit from social support intervention.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by an Institutional Development Award (IDeA) Center of Biomedical Research Excellence from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM113125. The funders of this research had no role in study design, data collection, management and analysis, nor with manuscript preparation.

Biography

Bygrave is a research neuropsychologist and assistant professor of psychology.

Gerassimakis is a radiologist and project coordinator.

Mwendwa is a clinical health psychologist and professor of psychology.

Erus is a radiology senior research investigator.

Davatzikos is a professor of radiology and director of the Center for Biomedical Image Computing and Analytics.

Wright is an associate professor of Nursing, research neuropsychologist and study principal investigator.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

  1. Apostolova LG, Mosconi L, Thompson PM, Green AE, Hwang KS, Ramirez A, Mistur R, Tsui WH, & de Leon MJ (2008). Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiology of Aging, 31(7), 1077–1088. 10.1016/j.neurobiolaging.2008.08.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Asman A, Akhondi-Asl A, Wang H, Tustison N, Avants B, Warfield SK, & Landman B (2013). MICCAI 2013 segmentation algorithms, theory and applications (SATA) challenge results summary. In MICCAI 2013 Challenge Workshop on Segmentation: Algorithms, Theory and Applications (SATA). [Google Scholar]
  3. Barnes J, Bartlett JW, van de Pol LA, Loy CT, Scahill RI, Frost C, Thompson P, & Fox NC (2009). A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease. Neurobiology of Aging, 30(11), 1711–1723. 10.1016/j.neurobiolaging.2008.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barnes LL, Lewis TT, Begeny CT, Yu L, Bennett DA, & Wilson RS (2012). Perceived discrimination and cognition in older African Americans. Journal of the International Neuropsychological Society: JINS, 18(5), 856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Beck AT, Steer RA, & Brown GK (1996). Beck Depression Inventory-II. The Psychological Corporation. [Google Scholar]
  6. Blanken AE, Hurtz S, Zarow C, Biado K, Honarpisheh H, Somme J, Brook J, Tung S, Kraft E, Lo D, Ng DW, Vinters HV, & Apostolova LG (2017). Associations between hippocampal morphometry and neuropathologic markers of Alzheimer’s disease using 7T MRI. NeuroImage Clinical, 15, 56–61. 10.1016/j.nicl.2017.04.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brown DL (2008). African American resiliency: Examining racial socialization and social support as protective factors. Journal of Black Psychology, 34(1), 32–48. 10.1177/0095798407310538 [DOI] [Google Scholar]
  8. Carmichael O, & Newton R Jr. (2019). Brain MRI findings related to Alzheimer’s disease in older African American adults. Progress in Molecular Biology and Translational Science, 165, 3–23. 10.1016/bs.pmbts.2019.04.002 [DOI] [PubMed] [Google Scholar]
  9. Carnethon MR, Pu J, Howard G, Albert MA, Anderson C, Bertoni AG, Mujahid MS, Palaniappan L, Taylor HA Jr, Willis M, & Yancy CW, & American Heart Association Council on Epidemiology and Prevention; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; and Stroke Council. (2017). Cardiovascular health in African Americans: A scientific statement from the American Heart Association. Circulation, 136(21), e393–e423. 10.1161/CIR.0000000000000534 [DOI] [PubMed] [Google Scholar]
  10. Chatters LM, Taylor RJ, Bullard KM, & Jackson JS (2009). Race and ethnic differences in religious involvement: African Americans, Caribbean Blacks and non-Hispanic Whites. Ethnic and Racial Studies, 32(7), 1143–1163. 10.1080/01419870802334531 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Clark R, Anderson NB, Clark VR, & Williams DR (1999). Racism as a stressor for African Americans. A biopsychosocial model. The American Psychologist, 54(10), 805–816. 10.1037//0003-066x.54.10.805 [DOI] [PubMed] [Google Scholar]
  12. Cohen S (2004). Social relationships and health. The American Psychologist, 59(8), 676–684. 10.1037/0003-066X.59.8.676 [DOI] [PubMed] [Google Scholar]
  13. Cohen S, Mermelstein R, Kamarck T, & Hoberman H (1985). Measuring the functional components of social support. In Sarason IG & Sarason B (Eds.), Social support: Theory, research, and applications (pp. 73–94). Martinus Nijhoff. 10.1007/978-94-009-5115-0_5 [DOI] [Google Scholar]
  14. Cohen S, & Wills TA (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. 10.1037/0033-2909.98.2.310 [DOI] [PubMed] [Google Scholar]
  15. Cotton K, Verghese J, & Blumen HM (2020). Gray matter volume covariance networks, social support, and cognition in older adults. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 75(6), 1219–1229. 10.1093/geronb/gbz023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Crews DC, Charles RF, Evans MK, Zonderman AB, & Powe NR (2010). Poverty, race, and CKD in a racially and socioeconomically diverse urban population. American Journal of Kidney Diseases, 55(6), 992–1000. 10.1053/j.ajkd.2009.12.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Davidson RJ, & McEwen BS (2012). Social influences on neuroplasticity: Stress and interventions to promote well-being. Nature Neuroscience, 15(5), 689–695. 10.1038/nn.3093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dawson AZ, Walker RJ, Campbell JA, & Egede LE (2015). Effect of perceived racial discrimination on self-care behaviors, glycemic control, and quality of life in adults with type 2 diabetes. Endocrine, 49(2), 422–428. 10.1007/s12020-014-0482-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Desai AK, Grossberg GT, & Chibnall JT (2010). Healthy brain aging: A road map. Clinics in Geriatric Medicine, 26(1), 1–16. 10.1016/j.cger.2009.12.002 [DOI] [PubMed] [Google Scholar]
  20. Dolezsar CM, McGrath JJ, Herzig AJ, & Miller SB (2014). Perceived racial discrimination and hypertension: A comprehensive systematic review. Health Psychology, 33, 20–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Doshi J, Erus G, Ou Y, Gaonkar B, & Davatzikos C (2013). Multi-atlas skull-stripping. Academic Radiology, 20(12), 1566–1576. 10.1016/j.acra.2013.09.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Doshi J, Erus G, Ou Y, Resnick SM, Gur RC, Gur RE, Satterthwaite TD, Furth S, Davatzikos C, & Alzheimer’s Neuroimaging Initiative. (2016). MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection. NeuroImage, 127, 186–195. 10.1016/j.neuroimage.2015.11.073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Erten-Lyons D, Woltjer RL, Dodge H, Nixon R, Vorobik R, Calvert JF, Leahy M, Montine T, & Kaye J (2009). Factors associated with resistance to dementia despite high Alzheimer disease pathology. Neurology, 72(4), 354–360. 10.1212/01.wnl.0000341273.18141.64 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Evans TE, Adams HHH, Licher S, Wolters FJ, van der Lugt A, Ikram MK, O’Sullivan MJ, Vernooij MW, & Ikram MA (2018). Subregional volumes of the hippocampus in relation to cognitive function and risk of dementia. NeuroImage, 178, 129–135. 10.1016/j.neuroimage.2018.05.041 [DOI] [PubMed] [Google Scholar]
  25. Folstein MF, Folstein SE, & McHugh PR (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. 10.1016/0022-3956(75)90026-6 [DOI] [PubMed] [Google Scholar]
  26. Fotuhi M, Do D, & Jack C (2012). Modifiable factors that alter the size of the hippocampus with ageing. Nature Reviews. Neurology, 8(4), 189–202. 10.1038/nrneurol.2012.27 [DOI] [PubMed] [Google Scholar]
  27. Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, & Kolson DL (2002). Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR. American Journal of Neuroradiology, 23(8), 1327–1333. [PMC free article] [PubMed] [Google Scholar]
  28. Geronimus AT, Gianaros PJ, Greer PJ, Ryan CM, & Jennings JR (2006). Higher blood pressure predicts lower regional grey matter volume: Consequences on short-term information processing. Neuroimage, 31(2), 754–765. 10.1016/j.neuroimage.2006.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Gianaros PJ, Greer PJ, Ryan CM, & Jennings JR (2006). Higher blood pressure predicts lower regional grey matter volume: Consequences on short-term information processing. NeuroImage, 31(2), 754–765. 10.1016/j.neuroimage.2006.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Glymour MM, & Manly JJ (2008). Lifecourse social conditions and racial and ethnic patterns of cognitive aging. Neuropsychology Review, 18(3), 223–254. 10.1007/s11065-008-9064-z [DOI] [PubMed] [Google Scholar]
  31. Goukasian N, Porat S, Blanken A, Avila D, Zlatev D, Hurtz S, Hwang KS, Pierce J, Joshi SH, Woo E, & Apostolova LG (2019). Cognitive correlates of hippocampal atrophy and ventricular enlargement in adults with or without mild cognitive impairment. Dementia and Geriatric Cognitive Disorders Extra, 9(2), 281–293. 10.1159/000490044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hafkemeijer A, Altmann-Schneider I, de Craen AJ, Slagboom PE, van der Grond J, & Rombouts SA (2014). Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults. Aging Cell, 13(6), 1068–1074. 10.1111/acel.12271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hatchett SJ, & Jackson JS (1993). African American extended kin systems: An assessment. In McAdoo HP (Ed.), Family ethnicity: Strength in diversity (pp. 90–108). Sage. [Google Scholar]
  34. Hayes AF, & Coutts JJ (2020). Use omega rather than Cronbach’s alpha for estimating reliability. But .... Communication Methods and Measures, 14(1), 1–24. 10.1080/19312458.2020.1718629 [DOI] [Google Scholar]
  35. Hostinar CE, & Gunnar MR (2015). Social support can buffer against stress and shape brain activity. AJOB Neuroscience, 6(3), 34–42. 10.1080/21507740.2015.1047054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Johnson KE, Sol K, Sprague BN, Cadet T, Muñoz E, & Webster NJ (2020). The impact of region and urbanicity on the discrimination-cognitive health link among older Blacks. Research in Human Development, 17(1), 4–19. 10.1080/15427609.2020.1746614 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kim HK, & McKenry PC (1998). Social networks and support: A comparison of African Americans, Asian Americans, Caucasians, and Hispanics. Journal of Comparative Family Studies, 29(2), 313–334. [Google Scholar]
  38. Kurth F, Cherbuin N, & Luders E (2017). The impact of aging on subregions of the hippocampal complex in healthy adults. NeuroImage, 163, 296–300. 10.1016/j.neuroimage.2017.09.016 [DOI] [PubMed] [Google Scholar]
  39. Lee E-KO, & Sharpe T (2007). Understanding religious/spiritual coping and support resources among African American older adults: A mixed-method approach. Journal of Religion, Spirituality & Aging, 19(3), 55–75. 10.1300/J496v19n03_05 [DOI] [Google Scholar]
  40. Levine ME, & Crimmins EM (2014). Evidence of accelerated aging among African Americans and its implications for mortality. Social Science & Medicine (1982), 118, 27–32. 10.1016/j.socscimed.2014.07.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lewis TT, Cogburn CD, & Williams DR (2015). Self-reported experiences of discrimination and health: Scientific advances, ongoing controversies, and emerging issues. Annual Review of Clinical Psychology, 11, 407–440. 10.1146/annurev-clinpsy-032814-112728 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Montagrin A, Saiote C, & Schiller D (2018). The social hippocampus. Hippocampus, 28(9), 672–679. 10.1002/hipo.22797 [DOI] [PubMed] [Google Scholar]
  43. Mustillo S, Krieger N, Gunderson EP, Sidney S, McCreath H, & Kiefe CI (2004). Self-reported experiences of racial discrimination and Black-White differences in preterm and low-birthweight deliveries: The CARDIA study. American Journal of Public Health, 94, 2125–2131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Paradies Y (2006). A systematic review of empirical research on self-reported racism and health. International Journal of Epidemiology, 35(4), 888–901. 10.1093/ije/dyl056 [DOI] [PubMed] [Google Scholar]
  45. Peek MK, & O’Neill GS (2001). Networks in later life: An examination of race differences in social support networks. The International Journal of Aging and Human Development, 52(3), 207–229. 10.2190/F1Q1-JV7D-VN77-L6WX [DOI] [PubMed] [Google Scholar]
  46. Rzezak P, Squarzoni P, Duran FL, de Toledo Ferraz Alves T, Tamashiro-Duran J, Bottino CM, Ribeiz S, Lotufo PA, Menezes PR, Scazufca M, & Busatto GF (2015). Relationship between brain age-related reduction in gray matter and educational attainment. PLOS One, 10(10), e0140945. 10.1371/journal.pone.0140945 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Salat DH, Chen JJ, van der Kouwe AJ, Greve DN, Fischl B, & Rosas HD (2011). Hippocampal degeneration is associated with temporal and limbic gray matter/white matter tissue contrast in Alzheimer’s disease. NeuroImage, 54(3), 1795–1802. 10.1016/j.neuroimage.2010.10.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Schröder J, & Pantel J (2016). Neuroimaging of hippocampal atrophy in early recognition of Alzheimer’s disease—A critical appraisal after two decades of research. Psychiatry Research. Neuroimaging, 247, 71–78. 10.1016/j.pscychresns.2015.08.014 [DOI] [PubMed] [Google Scholar]
  49. Sherman SM, Cheng Y, Fingerman KL, & Schnyer DM (2016). Social support, stress and the aging brain. Social Cognitive and Affective Neuroscience, 11(7), 1050–1058. 10.1093/scan/nsv071 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Steenland K, Goldstein FC, Levey A, & Wharton W (2016). A meta-analysis of Alzheimer’s disease incidence and prevalence comparing African-Americans and Caucasians. Journal of Alzheimer’s Disease: JAD, 50(1), 71–76. 10.3233/JAD-150778 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Taylor RJ, & Chatters LM (1988). Church members as a source of informal social support. Review of Religious Research, 30, 192–203. https://www.jstor.org/stable/3511355 [Google Scholar]
  52. Taylor RJ, Mouzon DM, Nguyen AW, & Chatters LM (2016). Reciprocal family, friendship and church support networks of African Americans: Findings from the national survey of American life. Race and Social Problems, 8(4), 326–339. 10.1007/s12552-016-9186-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Turner AD, James BD, Capuano AW, Aggarwal NT, & Barnes LL (2017). Perceived stress and cognitive decline in different cognitive domains in a cohort of older African Americans. The American Journal of Geriatric Psychiatry, 25(1), 25–34. 10.1016/j.jagp.2016.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Ulrich-Lai YM, & Herman JP (2009). Neural regulation of endocrine and autonomic stress responses. Nature Reviews Neuroscience, 10(6), 397–409. 10.1038/nrn2647 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Whalley LJ, Deary IJ, Appleton CL, & Starr JM (2004). Cognitive reserve and the neurobiology of cognitive aging. Ageing Research Reviews, 3(4), 369–382. 10.1016/j.arr.2004.05.001 [DOI] [PubMed] [Google Scholar]
  56. Williams DR, Lawrence JA, & Davis BA (2019). Racism and health: Evidence and needed research. Annual Review of Public Health, 40, 105–125. 10.1146/annurev-publhealth-040218-043750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, & Yamashita K (2000). Gender effects on age-related changes in brain structure. American Journal of Neuroradiology, 21(1), 112. http://www.ajnr.org/cgi/content/abstract/21/1/112 [PMC free article] [PubMed] [Google Scholar]
  58. Zanchi D, Giannakopoulos P, Borgwardt S, Rodriguez C, & Haller S (2017). Hippocampal and Amygdala Gray Matter loss in elderly controls with subtle cognitive decline. Frontiers in Aging Neuroscience, 9, 50. 10.3389/fnagi.2017.00050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Zhou H, Li R, Ma Z, Rossi S, Zhu X, & Li J (2016). Smaller gray matter volume of hippocampus/parahippocampus in elderly people with subthreshold depression: A cross-sectional study. BMC Psychiatry, 16(1), 219. 10.1186/s12888-016-0928-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Zimmerman ME, Ezzati A, Katz MJ, Lipton ML, Brickman AM, Sliwinski MJ, & Lipton RB (2016). Perceived stress is differentially related to hippocampal subfield volumes among older adults. PLOS One, 11(5), e0154530. 10.1371/journal.pone.0154530 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Zolochevska O, & Taglialatela G (2016). Non-demented individuals with Alzheimer’s disease neuropathology: Resistance to cognitive decline may reveal new treatment strategies. Current Pharmaceutical Design, 22(26), 4063–4068. 10.2174/1381612822666160518142110 [DOI] [PMC free article] [PubMed] [Google Scholar]

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