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Hindawi Publishing Corporation
Journal of Aging Research
Volume 2012, Article ID 512714, 9pages
doi:10.1155/2012/512714
Research Article
Association of Social Engagement with Brain Volumes
Assessed by Structural MRI
Bryan D. James,1Thomas A. Glass,2Brian Caffo,3Jennifer F. Bobb,3Christos Davatzikos,4
David Yousem,5and Brian S. Schwartz2, 6, 7
1Rush Alzheimer’s Disease Center, Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
2Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
3Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
4Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
5Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
6Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
7Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
Correspondence should be addressed to Bryan D. James, bryan james@rush.edu
Received 11 June 2012; Accepted 2 August 2012
Academic Editor: Alan J. Gow
Copyright © 2012 Bryan D. James et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We tested the hypothesis that social engagement is associated with larger brain volumes in a cohort study of 348 older male former
lead manufacturing workers (n=305) and population-based controls (n=43), age 48 to 82. Social engagement was measured
using a summary scale derived from confirmatory factor analysis. The volumes of 20 regions of interest (ROIs), including total
brain, total gray matter (GM), total white matter (WM), each of the four lobar GM and WM, and 9 smaller structures were derived
from T1-weighted structural magnetic resonance images. Linear regression models adjusted for age, education, race/ethnicity,
intracranial volume, hypertension, diabetes, and control (versus lead worker) status. Higher social engagement was associated
with larger total brain and GM volumes, specifically temporal and occipital GM, but was not associated with WM volumes except
for corpus callosum. A voxel-wise analysis supported an association in temporal lobe GM. Using longitudinal data to discern
temporal relations, change in ROI volumes over five years showed null associations with current social engagement. Findings are
consistent with the hypothesis that social engagement preserves brain tissue, and not consistent with the alternate hypothesis that
persons with smaller or shrinking volumes become less socially engaged, though this scenario cannot be ruled out.
1. Introduction
Social engagement, the performance of meaningful social
roles for either leisure or productive activity, has been shown
to be associated with better cognitive function and lowered
rates of cognitive decline and dementia in older adults [1–4].
Yet many questions remain regarding how social engagement
can potentially get “under the skull” to preserve cognitive
abilities. Inconsistencies in measurement across studies is
frequent with a number of overlapping constructs such as
social activity [1,5], social networks [6,7], and social
support [8] linked to cognitive outcomes; each has been
theorized to affect the brain through separate mechanisms.
Yet the neurological mechanisms that could lead to preser-
vation of cognitive function remain unclear and perhaps
the largest obstacle is a lack of research to directly explore
the biological effects of social engagement on the brain. A
popular hypothesis is that social engagement helps to build
a brain reserve capacity that allows the brain to tolerate
neuropathologic damage due to aging or disease without
deterioration of cognitive abilities [9,10]. In a case of “use it
or lose it,” remaining socially engaged as one ages may build
this brain reserve through neuroplastic changes in the brain
such as attenuated neuronal loss, or increased synaptic count
[11–13] or the growth of new neurons [14]—all of which
could be reflected in an increase or attenuated shrinking
of brain volume. In the context of aging, larger brain
volumes are associated with better cognitive function [15,
16], and preservation of cognitive function in the face of
neuropathology [17]. Demonstrating a link between social
2 Journal of Aging Research
engagement and larger brain volumes would provide support
for the brain reserve hypothesis and our understanding of the
neurological mechanisms at play.
We examined the relationship between social engage-
ment and brain volumes using two complementary methods,
a region-of-interest (ROI) analysis to investigate recognized
anatomical brain regions, and voxel-based morphometry
(VBM) to explore unbiased associations across the entire
brain. Utilizing available longitudinal MRI data, we were also
able to evaluate whether change in ROI volumes over five
years prior to assessment of social engagement was associated
with current level of social engagement in order to better dis-
cern temporal relations. We hypothesized that more socially
engaged persons have larger brain volumes, especially for
GM, which was found to evidence larger age-related declines
in volume compared to WM in this population [18].
2. Methods
2.1. Study Population and Design. We use d d a ta fro m a s t u dy
of lead exposure and cognitive function in former employees
of a chemical manufacturing plant in the eastern United
States and population-based controls with no history of
occupational lead exposure [19].Thecontrolswereselected
from the same geographical residential as the former lead
workers resided in using random selection from a telephone
database and frequency-matched to lead workers for age,
education, and race [20]. We used data from the third phase
of this study when assessment of social engagement and
a second MRI were obtained from study participants; an
initial baseline structural MRI was acquired in phase 2, on
average 5 years earlier. Detailed methods for study design
and recruitment in phases 1 (1994–1997; 703 former lead
workers and 130 controls, mean age 56 years) [20,21]and
2 (2001–2003; 589 of 979 former lead workers and 67 of
131 controls completed MRI; mean age 56 at enrollment)
[22] are described elsewhere. During phase 3 (2005–2008),
396 participants returned for an additional study visit. All
phases of the study were reviewed and approved by the Johns
Hopkins Bloomberg School of Public Health Committee
on Human Research and written informed consent was
obtained from all participants.
During phase 3, participants who completed the first
MRI in phase 2 (589 former lead workers and 67 controls)
were invited for a second MRI; 317 (54%) former lead
workers and 45 (67%) controls completed a second MRI.
Thus, two MRIs were obtained from 362 participants, rep-
resenting 91% of the 396 participants who returned for
phase 3 of the study. Nine of these had poor quality scans,
leaving 353 participants with useable MRIs. Participants with
phase 3 MRIs were on average younger than participants
with no MRI or only a phase 2 MRI [18]. Five participants
had missing data on social engagement; our final analysis
included 348 participants.
2.2. Structural Magnetic Resonance Imaging Acquisition. A
3 T General Electric scanner was utilized for the phase
3 MRI. T1-weighted images were acquired using spoiled
gradient recalled acquisition (SPGR) in steady state sequence
(repetition time (TR) =21 ms, echo time (TE) =8 ms, field
of view (FOV) =24 cm, flip angle =30◦one excitation, voxel
size =0.9375 mm by 0.9375 mm by 1.5 mm, field of view
24 cm, matrix size 256 ×256). Methods for phase 2 MRI have
been previously published [22].
2.3. Image Analysis. Quantitative analysis of MR volumes
was completed using previously published methods [23].
First, extracranial tissue and brainstem structures were
stripped. A validated specialized image analysis method was
employed to segment the images into GM and WM. The
CLASSIC algorithm [24] employs a 4-dimensional segmen-
tation framework in which the first and second scans are con-
sidered jointly to minimize discrepancies between the two
segmentations. The segmented images provide quantitative
volumetric measures of total GM, WM, and brain (GM plus
WM) matter.
To obtain volumes of predefined ROIs, regional analysis
was performed via computerized template matching tech-
niques previously reported and validated [22,25]. In brief,
a computerized image analysis algorithm based on pattern
matching was used to warp a reference digital brain atlas
to each participant’s MRI. The resulting 20 nonmutually
exclusive ROIs included the volumes of total brain, total GM,
total WM, major lobar subdivisions, and a number of smaller
structures.
For the voxel-wise approach, regional analysis of volumes
examined in normalized space (RAVENS) was used to yield
brain maps for analysis of local volumetric differences not
constrained by apriorianatomic definitions [23]. This
method can provide confirmatory evidence of associations in
predefined regions or provide additional insights into areas
of the brain linked to social engagement that are not apparent
from using the ROI approach. Using previously published
methods [25], segmented images were transformed into a
standard coordinate space using an elastic deformation algo-
rithm. This procedure yields tissue density maps for GM and
WM whose values are direct measurements of local tissue
volumes. Associations of predictor variables with GM and
WM volumes could then be examined on a voxel-by-voxel
basis, not constrained by arbitrary anatomical boundaries,
thereby revealing spatial patterns of such associations.
2.4. Social Engagement. Social engagement was measured for
the first time in phase 3 of this study (i.e., at the time of
the second MRI). The measure of social engagement came
from the enacted function profile (EFP), a 20-item scale
designed to measure multiple domains of enacted functional
performance in older adults based on pre-existing theory
regarding the measurement of actual functional performance
(rather than theoretical functional capacity) in daily life [26].
The EFP asks respondents how often they have engaged in
a number of common daily activities over the past week or
month. To test our measurement theory and to correct for
random measurement error, we used confirmatory factor
analysis to examine the conditional independence of four
domains of enacted function (social engagement, commu-
nity involvement, self-care, and productive activities) and
to derive factor scores to represent our theorized latent
Journal of Aging Research 3
constructs. For the analysis, a factor-based score based on
the 8 social engagement items was generated using MPLUS
version 5. The social engagement items and scale details are
included in the appendix.
2.5. Statistical Analysis
2.5.1. ROI-Based Approach. We first evaluated whether
higher social engagement was associated with larger volumes
of predefined anatomical ROIs (dependent variable) in a
cross-sectional analysis of phase 3 data. Associations between
social engagement and brain volumes were examined using
linear ordinary least squares regression modeling, with a
separate model for each ROI. Because tibia lead is associated
with smaller brain volumes [22], but was only available
for lead workers, we first evaluated whether tibia lead level
altered the association of interest or if it was appropriate to
include both lead workers and controls in our main analyses
without adjusting for lead. Tibia lead was not associated
with social engagement and there was no evidence that the
association of social engagement and brain volumes differed
by control status or by tibia lead level, so we combined
former lead workers and controls in all subsequent analyses.
All models were adjusted for intracranial volume, hand-
edness, and control status (versus former lead workers).
Demographic and health factors that could confound the
relationship between social engagement and brain volume
included age (centered), race/ethnicity (all minorities versus
whites), education (five categories, with high school plus
trade school as the reference group), cardiovascular disease
risk factors known to be associated with brain pathology
(hypertension and diabetes), and tibia lead level (measured
in lead workers only). Effect modification by age, education,
race/ethnicity, cardiovascular risk factors, and control status
was evaluated using models with cross-product terms. To
facilitate comparisons across ROIs, standardized regression
coefficients are presented. Model diagnostics were performed
to examine model fit and influential points. Because the 20
ROIs are not independent, we did not adjust for multiple
comparisons in this analysis choosing instead to report
standard errors and unadjusted tests of associations. Analyses
were performed with SAS 9.1 statistical software.
2.5.2. ROI-Based Analysis to Address Temporality. To discern
temporal relations, we performed secondary analyses using
the available longitudinal data from the first and second
MRI. We modeled social engagement (measured at the time
of the second MRI) as the outcome variable regressed upon
the change in ROI volumes from first to second MRI (to
address whether change in brain volumes over five years is
associated with social engagement at the end of the interval).
Standardized regression coefficients are presented. Strength
of association and model fit for these models were compared
to our main models.
2.5.3. Voxel-Wise Approach. We next used voxel-based analy-
sis to identify areas of GM and WM associated with social
engagement. At each voxel we conducted linear regression
of the voxel volume versus social engagement controlling for
the aforementioned covariates. From the regression output
we obtained a t-statistic for each voxel. We identified 3-
dimensional clusters of 100 or more contiguous voxels
exceeding the statistical threshold t>3.11 (corresponding
to an uncorrected Pvalue <0.001). To address multiplicity,
we conducted a permutation test to assess the statistical
significance of each cluster with respect to the permutation
distribution of the largest cluster of suprathreshold t-
statistics. More specifically, for 250 repetitions, we permuted
the brain images (e.g., voxel volumes) across subjects,
keeping the covariate data fixed. Then for each permuted
dataset, we performed the same analysis as was done on the
original dataset, identifying the largest cluster of contiguous
voxels exceeding t>3.11. We finally obtained a Pvalue for
each cluster by calculating the proportion of repetitions for
which the size of the cluster in question was greater than or
equal to the largest cluster of the permuted data. This voxel-
wise analysis was conducted separately for the gray and white
matter maps.
3. Results
3.1. Descriptive Summary of Study Participants. Study par-
ticipants were 48 to 82 years of age (mean (S.D.) =65.2
(7.9)); the majority were white/non-Hispanic, had a high
school education plus trade school, were hypertensive, and
not diabetic (Tab l e 1 ). The oldest individuals and the most
educated were more socially engaged. Younger participants,
white non-Hispanic persons, and those without hyperten-
sion or diabetes had larger brain volumes. There was a
complex pattern of association between brain volumes and
levels of education, as persons with a graduate degree had
brain volumes similar to those with less than high school
education; both groups had smaller total brain volumes
compared with the high school plus trade school reference
group. Participants in both the lowest and highest education
groups were an average of 3.5 years older than participants in
the middle categories. Former lead workers were less socially
engaged than population-based controls but had larger brain
volumes on average.
3.2. Associations of Social Engagement with Brain Volumes
3.2.1. ROI-Based Method. Inferences did not significantly
differ for base models and fully adjusted models, so only fully
adjusted models are presented. Higher social engagement
was significantly associated with larger total brain volume
and total GM volume, as well as larger temporal and occipital
GM lobar volumes (Table 2 ), but not with total or lobar WM
ROIs. Among the other ROIs evaluated, social engagement
was only significantly associated with corpus callosum vol-
ume. There was no evidence of effect modification by age,
education, race/ethnicity, or cardiovascular risk factors on
relations of social engagement with ROI volumes.
3.2.2. Analysis to Discern Temporal Relationships. We e v a l u-
ated whether changes in ROI volumes from the first to second
MRI were associated with social engagement at the time of
the second MRI. Information on changes in brain volumes
4 Journal of Aging Research
Tab le 1: Descriptive statistics.
N(%) Social Engagement
(Range −0.79, 1.09) (SD) To t a l b r a i n v ol u m e
(Range 880.6, 1449.3) (SD)
All 348 (100%) 0.01 (0.33) 1137.4 (100.4)
Age
48–59 82 (23.6%) −0.07 (0.35) 1187.5 (94.6)
60–64 92 (26.4%) 0.04 (0.32) 1161.3 (90.6)
65–69 84 (24.1%) 0.02 (0.29) 1123.9 (81.8)
70–82 90 (25.9%) 0.07 (0.33) 1079.7 (100.4)
P=0.028 P<0.001
Race/ethnicity
White/Non-Hispanic 315 (90.5%) 0.02 (0.32) 1141.6 (99.9)
All other 33 (9.6%) −0.05 (0.40) 1097.1 (97.9)
P=0.25 P=0.015
Educational attainment
<High school 25 (7.2%) −0.05 (0.41) 1083.1 (74.7)
High school 90 (25.9%) −0.05 (0.32) 1139.8 (97.4)
High school + trade school 167 (48.0%) 0.03 (0.30) 1135.8 (101.6)
College degree 56 (16.1%) 0.04 (0.34) 1171.6 (92.2)
Graduate degree 10 (2.9%) 0.28 (0.41) 1086.1 (137.9)
P=0.018 P=0.002
Hypertension
Yes 180 (51.7%) 0.01 (0.32) 1116.6 (97.5)
No 168 (48.3%) 0.01 (0.33) 1159.6 (99.0)
P=0.97 P<0.001
Diabetes
Yes 54 (15.5%) −0.01 (0.29) 1098.8 (90.5)
No 294 (84.5%) 0.02 (0.34) 1144.4 (100.7)
P=0.59 P=0.002
Control status
Population-based control 43 (12.4%) 0.12 (0.29) 1103.5 (100.5)
Former lead worker 305 (87.6%) 0.00 (0.33) 1142.1 (99.6)
P=0.018 P=0.018
All P-values from analysis of variance (ANOVA) tests.
in this cohort has been previously reported [18]. Changes in
brain volumes over five years were not associated with social
engagement at the end of the interval, except for temporal
WM (P=0.034) (Tab l e 3 ).
3.2.3. Voxel-Based Method. Clusters of voxels were identi-
fied in both GM and WM where social engagement was
associated with larger voxel volume after adjustment for the
aforementioned covariates (Table 4 ,Figure 1). There were
twelve GM clusters of 100 or more voxels exceeding a
statistical threshold of t=3.11 (P<0.001). The largest
GM cluster was 4581 voxels (peak t=4.23, P<0.0001)
and the second largest was 2501 voxels (peak t=4.23, P<
0.0001). The permutation test-based Pvalues for the largest
two clusters were 0.05 and 0.14, respectively. Although social
engagement was not associated with total or lobar WM ROIs,
the VBM analysis identified six suprathreshold WM clusters
of more than 100 contiguous voxels. The largest two WM
clusters consisted of 3221 voxels (peak t=3.95, P<0.001)
and 2592 voxels (peak t=4.06, P<0.0001), respectively.
These clusters were localized to the interior regions near the
cerebral fissure. Applying the cluster-based permutation test,
the Pvalue for the largest cluster was 0.10.
As the VBM analysis was not constrained by anatomical
regions, there were a number of similarities as well as
differences. Significant GM clusters were observed in the
temporal lobe (Figure 1), the region found to have the
strongest association with social engagement in the ROI
analysis, but a number of clusters were observed in regions
not identified by the ROI analysis, including clusters in the
parietal lobe and cerebellum. Furthermore, there were no
Journal of Aging Research 5
Tab le 2: Adjusted associations between social engagement (independent variable) and ROI volumes (dependent variables).
ROI Mean volume (cc) Social engagement standardized coefficient Pvalue
Total brain volume 1137.96 0.037 0.011
Total gray matter (GM) 534.14 0.072 0.007
Total white matter (WM) 603.82 0.001 0.975
Gray matter lobes
Frontal GM 134.85 0.037 0.273
Temporal GM 96.58 0.083 0.009
Parietal GM 65.40 0.068 0.081
Occipital GM 45.56 0.076 0.048
White matter lobes
Frontal WM 201.47 0.008 0.761
Temporal WM 119.03 0.021 0.501
Parietal WM 106.08 0.026 0.456
Occipital WM 58.22 0.007 0.869
Smaller structures
Cerebellum 119.63 −0.013 0.763
Medial structures 80.63 0.047 0.135
Cingulate gyrus 21.02 0.045 0.269
Insula 13.94 0.008 0.865
Corpus callosum 11.89 0.127 0.004
Internal capsule 9.85 0.026 0.520
Hippocampus 7.42 −0.004 0.923
Amygdala 2.43 0.037 0.453
Entorhinal cortex 2.37 −0.016 0.756
From models adjusted for age, education, intracranial volume, race/ethnicity, hypertension, diabetes, handedness, and control status.
Bold: P≤0.05.
Gray matter
(a) Gray matter
White matter
(b) White matter
Figure 1: The highlighted zones indicate regions in which higher social activity was associated with larger brain volumes from voxel-based
morphometry analysis. Only clusters of 100+ voxels shown. See Tab l e 4 for cluster-specific statistics.
significant associations with lobar WM volumes in the ROI
analysis, but large significant clusters were observed in WM
in the VBM analysis. These were in the corpus callosum,
which aligns with the ROI analysis.
4. Discussion
These findings provide some of the first published evidence
that higher social engagement is associated with larger brain
volumes as assessed by structural MRI using ROI- and voxel-
based methods. In contrast, change in brain volumes over the
five-year-period was not associated with social engagement.
Therefore, these findings are consistent with the hypothesis
that social engagement preserves brain tissue, and provide
some evidence against the alternate hypothesis that persons
with smaller or shrinking volumes become less socially
engaged, although we cannot rule out the possibility that
changes in social engagement over a longer period than five
6 Journal of Aging Research
Tab le 3: Adjusted associations between change in ROI volumes
(independent variable) and social engagement (dependent vari-
able).
ROI ΔROI standardized
coefficient1Pvalue
Total brain volume 0.023 0.713
Total gray matter (GM) 0.062 0.299
Total white matter (WM) −0.039 0.494
Gray matter lobes
Frontal GM 0.032 0.599
Temporal GM 0.112 0.055
Parietal GM 0.092 0.133
Occipital GM −0.011 0.849
White matter lobes
Frontal WM 0.010 0.851
Temp o r a l W M −0.115 0.034
Parietal WM −0.035 0.526
Occipital WM 0.046 0.416
Other structures (GM and WM)
Cerebellum 0.022 0.695
Medial structures 0.005 0.937
Cingulate gyrus −0.023 0.696
Insula −0.014 0.803
Corpus callosum −0.019 0.730
Internal capsule 0.067 0.244
Hippocampus 0.005 0.934
Amygdala 0.050 0.368
Entorhinal cortex 0.027 0.633
From models adjusted for age, education, intracranial volume, race/ethnic-
ity, hypertension, diabetes, handedness, and control status.
1Social engagement at time of 2nd MRI was the dependent variable.
Bold: P≤0.05.
years may be associated with later volumes. The primary
associations were with temporal and occipital lobar GM
volumes, and likely as a result of this, with total GM and total
brain volumes. There were no associations with lobar WM
volumes. The findings support the brain reserve hypothesis
by providing evidence that social engagement is associated
with larger brain volumes in specific regions, which may in
turn help to preserve cognitive function at older ages.
There are a number of proposed biological mechanisms
by which social engagement could affect cognitive function
through changes in brain volume, especially in GM, the site
of the neuronal cell bodies and a variety of connections
between neural and glial tissues. Total brain volume loss
[27], GM loss [28], neuronal shrinkage [29], and synaptic
loss [30] are common consequences of aging. Neuronal and
synaptic loss, as well as accelerated gross atrophy, are well-
documented pathophysiologic correlates of early Alzheimer’s
disease [31]. Brain areas with larger volumes may be able
to tolerate more loss caused by aging or disease before
exhibiting declines in cognitive function because of a higher
number of remaining healthy neurons and synapses [32].
Social engagement may lead to larger brain volumes through
a decrease in neuronal death or shrinkage, neurogenesis in
certain areas of the brain [12], increased dendritic spine
growth or axonal rearrangement [33]. Other lines of research
provide support for a link between social engagement and
larger brain volumes, including associations between social
engagement and other aspects of brain pathology or function
and demonstrated neuroplastic increases in volume due to
human behavior such as activity and learning. Furthermore,
experiments with animals placed in enriched environments
with increased opportunity for learning, activity, and inter-
action with other animals have demonstrated neurogenesis,
synaptogensis, and reduced neuronal loss [11,13,34–36].
The localization of the social engagement-volume rela-
tionship should be interpreted with caution, but some
initial conjectures can be made. The association with social
engagement was strongest in GM in the right temporal
lobe. Facial and verbal recognition, long-term memory, and
personality features reside in the temporal lobe [37]. Loss
of GM in this area occurs during aging [28]. GM was not
significantly associated with social engagement in the frontal
lobe, which displays the most loss during brain aging [38].
Furthermore, no significant associations were found for
the hippocampus, a structure important to memory and
cognition and vulnerable to aging [39],thoughsomestudies
have shown preservation of hippocampal volumes with aging
[40]. Research on enriched environments in animal models
have found evidence of neurogenesis in the hippocampus,
[14] and a study in humans showed larger hippocampi in
socially engaged persons [41]. However, the hippocampus is
a small structure and it is likely measured with less accuracy
due to greater proportional error. The only WM structure
found to be significantly associated with social engagement
in the ROI-based analysis was the corpus callosum. There
is some evidence that hemispheric asymmetry is a marker
of reserve [42], and it is hypothesized that larger corpus
callosum volume (which facilitates communication across
hemispheres) may compensate for psychomotor slowing in
later life [43]. Moreover, it is possible that preservation of
lobar GM volumes could also preserve inter-hemispheric
connections between those areas resulting in a larger corpus
callosum.
Strengths of this study include the relatively large number
of subjects with MRIs, the robustness of brain imaging
analysis, the use of a rigorous measure of social engagement,
the availability of longitudinal data, and the ability to control
for important confounders. Conducting analyses using both
ROIs and VBM gave us two separate but complimentary ways
to examine the association between social engagement and
tissue volume in specific areas of the brain [15]. The ROI
analysis was informed by recognized anatomical structures
while the VBM analysis did not rely on aprioristructural
boundaries but rather examined the entire brain in an
unbiased region-by-region basis.
One important limitation was the lack of a baseline social
engagement measure, preventing us from examining the
association between social engagement and change in brain
structure or the association of change in social engagement
with later brain structure. Another limitation is the unique
nature of this cohort, which includes persons with past
Journal of Aging Research 7
Tab le 4: Cluster statistics from voxel-wise analysis.
XYZMaximum t-statistic Unadjusted Pvalue Cluster size Cluster Pvalue
Gray matter
−42 50 −54 4.23 0.00002 4581 0.05
−351−66 4.23 0.00001 2501 0.14
−65 −17 21 3.97 0.00004 1278 0.34
64 4 15 3.78 0.00009 1029 0.43
−46 −14 −63 4.16 0.00002 908 0.48
−56 −32 −64 4.08 0.00003 892 0.49
−65 17 −14 3.86 0.00007 511 0.68
−57 66 −25 3.9 0.00006 315 0.74
57 1 −46 3.73 0.00011 272 0.76
17 71 −31 3.54 0.00023 227 0.79
30 −37 32 3.47 0.00029 162 0.82
−50 14 −39 3.26 0.00062 119 0.82
White matter
24 −21 12 3.95 0.00005 3221 0.10
−14 18 3 4.06 0.00003 2592 0.14
23 20 9 4.12 0.00002 1904 0.23
13 −47 −15 3.45 0.00031 797 0.44
35 45 −21 3.46 0.00030 198 0.77
27 41 −6 3.33 0.00048 102 0.83
Displays cluster centroid (x-, y-, and z- MNI coordinates), maximum t-statistic within the cluster, Pvalue (unadjusted) of the maximum t-statistic, cluster
size in number of contiguous voxels, and permutation test-based cluster Pvalue. The cluster P-value compares the size of each suprathreshold cluster to the
permutation distribution of the largest cluster, thereby accounting for multiple comparisons. Only results from clusters of 100+ voxels shown.
Tab le 5: Social engagement assessment.
Item: in the last week/month have you ... Response scale Mean (std dev)
(i) been in touch with friends or relatives by phone or by letters? W 2.6 (1.2)
(ii) gotten your hair [MEN cut WOMEN done] or dressed up to go out at least once? M 2.5 (1.3)
(iii) done any unpaid volunteer work or community service? M 0.9 (1.5)
(iv) been out to have lunch or dinner with someone? M 2.8 (1.4)
(v) been to a meeting at a club, senior center, or organization in which you are active other
than religious institution? M 0.8 (1.2)
(vi) been out socially with friends or relatives, for example, to see a show, a party or holiday
celebration, or some other social event? M 1.2 (1.1)
(vii) gone shopping for food, clothes, or something else you needed? M 2.8 (1.2)
(viii) done any indoor or outdoor recreational activity like bowling, working out, fishing,
hiking, boating, swimming, golfing? M 1.8 (2.0)
occupational lead exposure. Although we found no evidence
that associations differedaftercontrolforleaddoseorin
comparing former lead workers to controls, the findings may
not be generalizable to the general older adult population.
The cohort is also racially and occupationally homogenous,
and all male. Thus, we have no information on social engage-
ment and the older female brain or differences across
race/ethnic groups. However, homogeneity in occupation
and socioeconomic status in this cohort may increase inter-
nal validity by lessening concerns of confounding by other
factors linked to brain reserve and correlated with social
engagement. Some potential confounders we were not able
to adjust for include genes, IQ, stress response, personality,
history of head injury, and MCI or prodromal dementia, all
of which could be associated with both social engagement
and brain structure. Finally, a limitation that could in part
relate to the observed lack of an association between social
engagement and WM is that we did remove white matter
lesions from volumetric measures prior to analysis.
For more than a decade, recommendations have been
made for older adults to stay socially engaged to keep their
brains healthy based on evidence from epidemiologic studies
of cognition and dementia. These studies have not addressed
the “black box” regarding how social engagement may be
related to the neuroanatomical substrate. Ours is an example,
in a community-dwelling older adult population, of this
8 Journal of Aging Research
Tab le 6: The 8 social engagement items form one factor, of four,
in the 20-item enacted function profile. Measurement properties of
the enacted function profile follow.
Chi-square test of model fit
Value 68.437
Degrees of freedom 46
Pvalue 0.018
CFI/TLI
Comparative fit index (CFI) 0.950
Tucker-lewis fit index (TLI) 0.946
RMSEA (Root mean square error of
approximation)
Estimate 0.035
WRMR (Weighted root mean square
residual)
Value 0.676
necessary piece to the puzzle of why socially engaged persons
are more cognitively intact at advanced ages.
Appendix
See Tables 5and 6.
Acknowledgments
The authors would like to thank the participants and staffof
the former lead workers study. This research was supported
by Grant R01 AG10785 from the National Institute on Aging.
Its content is solely the responsibility of the authors. B. D.
James was supported by training grant T32 AG000247 from
the National Institute on Aging and the Illinois Department
of Public Health. There are no disclosures to report.
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