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Association of Social Engagement with Brain Volumes Assessed by Structural MRI


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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.
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Hindawi Publishing Corporation
Journal of Aging Research
Volume 2012, Article ID 512714, 9pages
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
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 [14].
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 aect 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 eects 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
[1113] 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 =30one 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
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 dierences 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 diered
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). Eect 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
coecients 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 coecients 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
dier 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 eect 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)
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
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
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
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
dierences. 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 coecient 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: P0.05.
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-
ROI ΔROI standardized
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: P0.05.
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 aect 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 [11,13,3436].
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
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
35166 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 dieredaftercontrolforleaddoseorin
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 dierences 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
Comparative fit index (CFI) 0.950
Tucker-lewis fit index (TLI) 0.946
RMSEA (Root mean square error of
Estimate 0.035
WRMR (Weighted root mean square
Value 0.676
necessary piece to the puzzle of why socially engaged persons
are more cognitively intact at advanced ages.
See Tables 5and 6.
The authors would like to thank the participants and staof
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.
[1] B. D. James, R. S. Wilson, L. L. Barnes, and D. A. Bennett,
“Late-life social activity and cognitive decline in old age,
Journal of the International Neuropsychological Society, vol. 17,
no. 6, pp. 998–1005, 2011.
of social integration on preserving memory function in a
nationally representative US elderly population,American
Journal of Public Health, vol. 98, no. 7, pp. 1215–1220, 2008.
[3] S. S. Bassuk, T. A. Glass, and L. F. Berkman, “Social disengage-
ment and incident cognitive decline in community—dwelling
elderly persons,Annals of Internal Medicine, vol. 131, no. 3,
pp. 165–173, 1999.
[4] L. Fratiglioni, S. Paillard-Borg, and B. Winblad, “An active and
socially integrated lifestyle in late life might protect against
dementia,Lancet Neurology, vol. 3, no. 6, pp. 343–353, 2004.
[5] A. Karp, S. Paillard-Borg, H. X. Wang, M. Silverstein, B.
Winblad, and L. Fratiglioni, “Mental, physical and social com-
ponents in leisure activities equally contribute to decrease
dementia risk,Dementia and Geriatric Cognitive Disorders,
vol. 21, no. 2, pp. 65–73, 2006.
[6] L. L. Barnes, C. F. Mendes De Leon, R. S. Wilson, J. L. Bienias,
and D. A. Evans, “Social resources and cognitive decline in a
population of older African Americans and whites,Neurology,
vol. 63, no. 12, pp. 2322–2326, 2004.
[7] R.E.Holtzman,G.W.Rebok,J.S.Saczynski,A.C.Kouzis,K.
W. Doyle, and W. W. Eaton, “Social network characteristics
and cognition in middle-aged and older adults,Journals of
Gerontology B, vol. 59, no. 6, pp. P278–P284, 2004.
[8] T.E.Seeman,T.M.Lusignolo,M.Albert,andL.Berkman,
“Social relationships, social support, and patterns of cognitive
aging in healthy, high-functioning older adults: MacArthur
studies of successful aging,Health Psychology, vol. 20, no. 4,
pp. 243–255, 2001.
[9] L. Fratiglioni and H. X. Wang, “Brain reserve hypothesis in
dementia,Journal of Alzheimer’s Disease,vol.12,no.1,pp.
11–22, 2007.
[10] M. J. Valenzuela and P. Sachdev, “Brain reserve and cognitive
decline: a non-parametric systematic review,Psychological
Medicine, vol. 36, no. 8, pp. 1065–1073, 2006.
[11] G. Kempermann, D. Gast, and F. H. Gage, “Neuroplasticity
in old age: Sustained fivefold induction of hippocampal neu-
rogenesis by long-term environmental enrichment,Annals of
Neurology, vol. 52, no. 2, pp. 135–143, 2002.
[12] N. W. Milgram, C. T. Siwak-Tapp, J. Araujo, and E. Head,
“Neuroprotective eects of cognitive enrichment,Ageing
Research Reviews, vol. 5, no. 3, pp. 354–369, 2006.
[13] M. C. Diamond, “Response of the brain to enrichment,” Anais
da Academia Brasileira de Ciˆ
encias, vol. 73, pp. 211–220, 2001.
[14] G. Kempermann, H. G. Kuhn, and F. H. Gage, “More hip-
pocampal neurons in adult mice living in an enriched envi-
ronment,Nature, vol. 386, no. 6624, pp. 493–495, 1997.
[15] B. S. Schwartz, S. Chen, B. Cao et al., “Relations of brain
volumes with cognitive function in males 45 years and older
with past lead exposure,NeuroImage, vol. 37, no. 2, pp. 633–
641, 2007.
[16] N. T. Aggarwal, R. S. Wilson, J. L. Bienias et al., “The associ-
ation of magnetic resonance imaging measures with cognitive
function in a biracial population sample,Archives of Neurol-
ogy, vol. 67, no. 4, pp. 475–482, 2010.
[17] R. Katzman, R. Terry, R. DeTeresa et al., “Clinical, pathologi-
cal, and neurochemical changes in dementia: a subgroup with
preserved mental status and numerous neocortical plaques,
Annals of Neurology, vol. 23, no. 2, pp. 138–144, 1988.
[18] B. S. Schwartz, B. Cao, W. F. Stewart et al., “Evaluation of
cumulative lead dose and longitudinal changes in structural
magnetic resonance imaging in former organolead workers,
Journal of Occupational and Environmental Medicine, vol. 52,
no. 4, pp. 407–414, 2010.
[19] B.S.Schwartz,K.I.Bolla,W.Stewart,D.P.Ford,J.Agnew,and
H. Frumkin, “Decrements in neurobehavioral performance
associated with mixed exposure to organic and inorganic
lead,American Journal of Epidemiology, vol. 137, no. 9, pp.
1006–1021, 1993.
[20] B. S. Schwartz, W. F. Stewart, K. I. Bolla et al., “Past adult lead
exposure is associated with longitudinal decline in cognitive
function,Neurology, vol. 55, no. 8, pp. 1144–1150, 2000.
[21] W. F. Stewart, B. S. Schwartz, D. Simon, K. I. Bolla, A. C.
Todd, and J. Links, “Neurobehavioral function and tibial and
chelatable lead levels in 543 former organolead workers,
Neurology, vol. 52, no. 8, pp. 1610–1617, 1999.
[22] W. F. Stewart, B. S. Schwartz, C. Davatzikos et al., “Past adult
lead exposure is linked to neurodegeneration measured by
brain MRI,Neurology, vol. 66, no. 10, pp. 1476–1484, 2006.
[23] A. F. Goldszal, C. Davatzikos, D. L. Pham, M. X. H. Yan, R.
N. Bryan, and S. M. Resnick, “An image-processing system
for qualitative and quantitative volumetric analysis of brain
Journal of Aging Research 9
images,Journal of Computer Assisted Tomography, vol. 22, no.
5, pp. 827–837, 1998.
[24] Z. Xue, D. Shen, and C. Davatzikos, “CLASSIC: consistent
longitudinal alignment and segmentation for serial image
computing,NeuroImage, vol. 30, no. 2, pp. 388–399, 2006.
[25] D. Shen and C. Davatzikos, “HAMMER: Hierarchical attribute
matching mechanism for elastic registration,IEEE Transac-
tions on Medical Imaging, vol. 21, no. 11, pp. 1421–1439, 2002.
[26] T. A. Glass, “Conjugating the “tenses” of function: discordance
among hypothetical, experimental, and enacted function in
older adults,Gerontologist, vol. 38, no. 1, pp. 101–112, 1998.
[27] S.M.Resnick,D.L.Pham,M.A.Kraut,A.B.Zonderman,
and C. Davatzikos, “Longitudinal magnetic resonance imag-
ing studies of older adults: a shrinking brain,Journal of
Neurosc ience, vol. 23, no. 8, pp. 3295–3301, 2003.
[28] C. D. Smith, H. Chebrolu, D. R. Wekstein, F. A. Schmitt,
andW.R.Markesbery,“Ageandgendereects on human
brain anatomy: a voxel-based morphometric study in healthy
elderly,Neurobiology of Aging, vol. 28, no. 7, pp. 1075–1087,
[29] R. D. Terry, R. DeTeresa, and L. A. Hansen, “Neocortical cell
counts in normal human adult aging,Annals of Neurology,
vol. 21, no. 6, pp. 530–539, 1987.
[30] E. Masliah, M. Mallory, L. Hansen, R. DeTeresa, and R.
D. Terry, “Quantitative synaptic alterations in the human
neocortex during normal aging,” Neurology, vol. 43, no. 1, pp.
192–197, 1993.
[31] G. L. Wenk, “Neuropathologic changes in Alzheimer’s dis-
ease,Journal of Clinical Psychiatry, vol. 64, supplement 9, pp.
7–10, 2003.
[32] N. Scarmeas and Y. Stern, “Cognitive reserve and lifestyle,
Journal of Clinical and Experimental Neuropsychology, vol. 25,
no. 5, pp. 625–633, 2003.
[33] B. Draganski and A. May, “Training-induced structural chang-
es in the adult human brain,Behavioural Brain Research, vol.
192, no. 1, pp. 137–142, 2008.
[34] E. Gould, A. J. Reeves, M. S. A. Graziano, and C. G. Gross,
“Neurogenesis in the neocortex of adult primates,Science,
vol. 286, no. 5439, pp. 548–552, 1999.
[35] H. Van Praag, G. Kempermann, and F. H. Gage, “Neural Con-
sequences of environmental enrichment,Nature Re views
Neurosc ience, vol. 1, no. 3, pp. 191–198, 2000.
[36] C. T. Siwak-Tapp, E. Head, B. A. Muggenburg, N. W. Milgram,
and C. W. Cotman, “Region specific neuron loss in the aged
canine hippocampus is reduced by enrichment,Neurobiology
of Aging, vol. 29, no. 1, pp. 39–50, 2008.
[37]E.R.Kandel,J.H.Schwartz,andT.M.Jessell,Principles of
Neural Science, Appleton & Lange, East Norwalk, Conn, USA,
3rd edition, 1991.
[38] T. L. Jernigan, S. L. Archibald, C. Fennema-Notestine et al.,
“Eects of age on tissues and regions of the cerebrum and
cerebellum,Neurobiology of Aging, vol. 22, no. 4, pp. 581–594,
[39] R. I. Scahill, C. Frost, R. Jenkins, J. L. Whitwell, M. N. Rossor,
and N. C. Fox, “A longitudinal study of brain volume changes
in normal aging using serial registered magnetic resonance
imaging,Archives of Neurology, vol. 60, no. 7, pp. 989–994,
[40] C.D.Good,I.S.Johnsrude,J.Ashburner,R.N.A.Henson,
K. J. Friston, and R. S. J. Frackowiak, “A voxel-based morpho-
metric study of ageing in 465 normal adult human brains,
NeuroImage, vol. 14, no. 1 I, pp. 21–36, 2001.
[41] J. S. Saczynski, L. A. Pfeifer, K. Masaki et al., “The eect
of social engagement on incident dementia and hippocam-
pal volume: the Honolulu-Asia aging study,Alzheimer’s &
Dementia, vol. 1, no. 1, supplement, p. S27, 2005.
[42] R. Cabeza, N. D. Anderson, J. K. Locantore, and A. R.
McIntosh, “Aging gracefully: compensatory brain activity in
high-performing older adults,NeuroImage,vol.17,no.3,pp.
1394–1402, 2002.
[43] D. W. Desmond, J. T. Moroney, M. Sano, and Y. Stern,
“Mortality in patients with dementia after ischemic stroke,
Neurology, vol. 59, no. 4, pp. 537–543, 2002.
... Still, the link between brain structure and social connections, the umbrella term encompassing social isolation, social support and loneliness, has not received much attention(22). First cross-sectional studies have linked low social connection to an elevated "brain age" gap estimate(23), changes in microstructural (24)(25)(26) and volumetric measures in brain regions including the hippocampus and the prefrontal cortex (26)(27)(28)(29)(30)(31)(32). In a longitudinal study microstructural deteriorations and a larger total white matter hyperintensity volume were correlated with decreases in predominantly social activities. ...
... The copyright holder for this this version posted December 16, 2021. ; doi: medRxiv preprint lobe) (27) and Blumen and Verghese (hippocampus, precuneus, superior frontal gyrus, medial frontal gyrus) (29) found decreased volumes in regions we detected, too. ...
Full-text available
Background Social isolation is a risk factor for dementia, a devastating disease with a rapidly growing global prevalence. However, the link between social isolation and changes in brain structure and function is poorly understood, as studies are scarce in number, methodologically inconsistent and small in size. In this pre-registered analysis of a large population-based panel study, we aimed to determine the impact of social isolation on brain structures and cognitive functions central to age associated decline and dementia. Methods and findings We analysed data of 1992 cognitively healthy participants of the LIFE-Adult study at baseline (age range: 50-82 years) and of 1409 particpants at follow-up (average change in age: 5.89 years). We measured social isolation using the 30-point Lubben Social Network Scale (LSNS) and derived measures of grey matter structure from anatomical 3T MRIs. We employed covariate adjusted linear mixed models to test the associations of baseline social isolation and change in social isolation with hippocampal volume, cognitive functions (executive functions, memory, processing speed) and cortical thickness. We found stronger baseline social isolation to be significantly associated with smaller hippocampal volumes (β = −5.5 mm3/LSNS point(pt), FDR q = 0.004, BF = 14.6) and lower cognitive functions (all β < −0.014 SD/pt, FDR q < 0.003, BF > 49). Increases in social isolation over time were linked to hippocampal volume decline (β = −4.9 mm3/pt, FDR q = 0.01, BF = 2.9) and worse memory performance (β = −0.013 SD/pt, FDR q = 0.04, BF = 1.1). Furthermore, we detected a significant interaction of baseline social isolation with change in age on hippocampal volume (β = −0.556 mm3/pt*a, q = 0.04, BF = 0.5), indicating accelerated brain aging in more isolated individuals. Moreover, social isolation cross-sectionally and longitudinally correlated with lower cortical thickness in multiple clusters in the orbitofrontal cortex, precuneus and other areas (FDR q < 0.05). Conclusions Here, we provide evidence that social isolation contributes to hippocampal and cortical atrophy and subtle cognitive decline in non-demented mid- to late-life adults. Importantly, within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity for targeting social isolation to reduce dementia risk.
... Apparently, smaller brain volumes associated with SI reflects neuronal and synaptic loss, as well as accelerated gross atrophy, which are correlated with early Alzheimer's disease. (James et al., 2012). SI also could aggravate the oxidative and inflammatory processes mediated damage in hippocampus and frontal cortex leading to abnormal neurotransmission, synaptic plasticity and neuronal apoptosis in AD patients. ...
... Epigallocatechin-3-gallate (EGCG), is the main polyphenolic constituent of green tea that has been found to protect neuron-like cells against Aβ-mediated toxicity and increase secreted levels of amyloid precursor protein-α (APP-α) (James et al., 2012). The antioxidant and anti-inflammatory properties of EGCG may be attributed to its capability of scavenging of free radicals along with attenuating the deamination of monoamine leading to protection of glial and neuronal cells from determinant effects of oxidative stress production and inflammatory cascades (Ahmed et al., 2002). ...
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Background Alzheimer's disease (AD) is the most common cause of dementia where symptoms gradually deteriorate over years. We previously reported that social isolation exacerbates memory loss while mental and physical activities maintain cognitive functions. Epigallocatechin-3-gallate (EGCG), a natural chelator, and wheatgrass, natural antioxidant could provide CNS health-promoting effects. Purpose The study aimed to investigate the combined effects of EGCG and wheatgrass together with mental and physical activities against AD in socialized and isolated conditions. Study design Rats were classified into socialized and corresponding isolated groups. Methodology Eight groups of rats (4 socialized and 4 isolated) were exposed to mental and physical activities using Swimming test and Y-maze (each for one time/week) during four weeks of the experiment. Two groups of both socialized and isolated were normal while the others (AD model) received daily AlCl3 (70 mg/kg IP). Normal and AD model groups received also either saline for control or EGCG (10 mg/kg every other day IP) & wheat grass (100 mg/kg PO daily) for treated groups. The brain Aβ, AChE, monoamines, inflammatory mediators, oxidative parameters and brain derived neurotrophic factor (BDNF) were measured. Histopathological examination of brain sections was also conducted. Results Brain neurological damage is more severe in isolation-associated AD. Treatment of EGCG and wheatgrass together with mental and physical activities (Ph & M) showed higher protection against hazards of AlCl3 and isolation than mental & physical activities alone especially in isolated groups. Their protection was indicated by the significant decrease in Aβ, AChE, MDA, TNF-α, IL-1β together with the increase in SOD, TAC, brain monoamines, BDNF which confirmed by improvement in histopathological pictures of brain sections. Conclusion It was concluded that, EGCG and wheatgrass together with mental and physical activities can protect against neuronal degenerations associated with the development of AD particularly in social isolated conditions than mental and physical activities alone.
... There are even several potential biological mechanisms, which can explain how social relationships could preserve cognitive function during ageing and help to reduce the risk for cognitive decline and dementia, e.g., brain-derived neurotrophic factors (BDNF) [21]. Studies on social relationships have found further potential protective effects from having a good social network, including lower levels of systemic inflammation [22], lower proportion of depression [23], as well as larger total brain-and grey matter volume [24]. ...
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Background This study aimed to investigate the longitudinal associations between social network (SN) and the risk of lower cognitive function, mild cognitive impairment (MCI), and dementia among cognitively normal individuals 65 years and older. Methods Data from the Age, Gene/Environment Susceptibility (AGES) Reykjavik Study on 2816 participants (aged 65 to 96 years) were used to examine the associations using multiple logistic and linear regression models. SN included questions on frequency of contact with family and friends as well as information on marital status, resulting in a score ranging from 0 (poor social network) to 3 (good social network). Cognitive function outcomes included the speed of processing (SP), executive function (EF) and memory function (MF). MCI and dementia were diagnosed using a detailed assessment according to international guidelines. Results At baseline 0.5, 7.0, 41.7 and 50.8% reported a score of 0, 1, 2 and 3, respectively. During a mean follow-up time of 5.2 years, 7.1% (n = 188) of cognitively intact participants developed MCI and 3.0% (n = 79) developed dementia. Longitudinal analyses demonstrated that participants who had low SN were significantly more likely to have declines in MF (β = − 0.533, P = 0.014) compared to high SN. Social networks were not independently associated with the decline of SP and EF during follow-up. According to fully adjusted models using logistic regression, SN was significantly associated with incidence risk of MCI (OR = 2.030, P = 0.014 and OR = 1.847 P = 0.001). These associations were largely independent of other lifestyle factors, depression and genetic disposition. Conclusions Community-dwelling older adults who have poor social networks have a higher risk of declining memory function as well as a higher risk of mild cognitive impairment than older adults who have a higher social network. This study included numbers of relevant covariates in the study analysis, thereby significantly contributing to the literature on cognitive aging.
... This finding offers partial support for our second hypothesis. Two previous studies have also reported a relationship between higher social activity levels and higher GM volume within this region (James et al., 2012;Arenaza-Urquijo et al., 2016), consistent with the "brain maintenance" (Nyberg et al., 2012) or "brain reserve" (Stern, 2012) hypotheses. However, other studies have not replicated this association (Foubert-Samier et al., 2012;Seider et al., 2016;Anatürk et al., 2020), or otherwise report associations across frontal, parietal and temporal regions (Valenzuela et al., 2008;Arenaza-Urquijo et al., 2015;Seider et al., 2016). ...
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Introduction: This study aimed to evaluate whether engagement in leisure activities is linked to measures of brain structure, functional connectivity, and cognition in early old age. Methods: We examined data collected from 7,152 participants of the United Kingdom Biobank (UK Biobank) study. Weekly participation in six leisure activities was assessed twice and a cognitive battery and 3T MRI brain scan were administered at the second visit. Based on responses collected at two time points, individuals were split into one of four trajectory groups: (1) stable low engagement, (2) stable weekly engagement, (3) low to weekly engagement, and (4) weekly to low engagement. Results: Consistent weekly attendance at a sports club or gym was associated with connectivity of the sensorimotor functional network with the lateral visual (β = 0.12, 95%CI = [0.07, 0.18], FDR q = 2.48 × 10 –3 ) and cerebellar (β = 0.12, 95%CI = [0.07, 0.18], FDR q = 1.23 × 10 –4 ) networks. Visiting friends and family across the two timepoints was also associated with larger volumes of the occipital lobe (β = 0.15, 95%CI = [0.08, 0.21], FDR q = 0.03). Additionally, stable and weekly computer use was associated with global cognition (β = 0.62, 95%CI = [0.35, 0.89], FDR q = 1.16 × 10 –4 ). No other associations were significant (FDR q > 0.05). Discussion: This study demonstrates that not all leisure activities contribute to cognitive health equally, nor is there one unifying neural signature across diverse leisure activities.
... First, the hippocampus is among the earliest areas of the brain affected by AD (Pennanen et al., 2004), allowing us to detect neuropathology in our sample that is largely asymptomatic. Second, hippocampal volume is linked to dementia (James et al., 2012;Kaye et al., 1997). Finally, this area is implicated in social memory (Kaye et al., 1997). ...
Access to cognitive stimulation through social interactions is a key mechanism used to explain the association between personal networks, cognitive health, and brain structure in older adults. However, little research has assessed how best to operationalize access to novel or diverse social stimuli using social network measures, many of which were designed to study information diffusion within large whole networks (e.g., structural holes and bridging social capital). Using data from 277 adults in [redacted for review], we aimed to evaluate such measures for use in research on cognitive aging using personal social networks. We found a positive association between individual measures of structural holes and cognitive health, but not with brain structure. Further, we extracted a latent measure of bridging social capital using multiple individual measures (i.e., structural holes, network diversity, weak ties, and network size) and found it was significantly associated with cognitive health and brain structure, supporting the utility of this concept and related measures in the study of cognitive aging. Finally, individual measures may underestimate the effects of multidimensional bridging social capital on cognitive health and brain structure compared to a latent measure that combines them.
... In humans, decreased social interaction and often attendant feelings of loneliness have been linked to factors that may increase risk of cognitive impairment and dementia, including worsened sleep [140,141] and increased stress [140], inflammation [141][142][143][144], and systolic blood pressure [145]. Conversely, higher levels of social engagement in older adults provide increased opportunities for cognitive stimulation, problem solving, and social support, and have been linked to increased total brain and grey matter volumes [146], greater grey matter integrity in several regions relevant to social cognition [147], and increased neural network function, which boosts cognitive reserve [148]. Social network size and level of social engagement have also been shown to modify the relationship between AD pathology and cognitive function [149,150]. ...
Full-text available
A decade has passed since we published a comprehensive review in this journal addressing the topic of promoting successful cognitive aging, making this a good time to take stock of the field. Because there have been limited large-scale, randomized controlled trials, especially following individuals from middle age to late life, some experts have questioned whether recommendations can be legitimately offered about reducing the risk of cognitive decline and dementia. Despite uncertainties, clinicians often need to at least make provisional recommendations to patients based on the highest quality data available. Converging lines of evidence from epidemiological/cohort studies, animal/basic science studies, human proof-of-concept studies, and human intervention studies can provide guidance, highlighting strategies for enhancing cognitive reserve and preventing loss of cognitive capacity. Many of the suggestions made in 2010 have been supported by additional research. Importantly, there is a growing consensus among major health organizations about recommendations to mitigate cognitive decline and promote healthy cognitive aging. Regular physical activity and treatment of cardiovascular risk factors have been supported by all of these organizations. Most organizations have also embraced cognitively stimulating activities, a heart-healthy diet, smoking cessation, and countering metabolic syndrome. Other behaviors like regular social engagement, limiting alcohol use, stress management, getting adequate sleep, avoiding anticholinergic medications, addressing sensory deficits, and protecting the brain against physical and toxic damage also have been endorsed, although less consistently. In this update, we review the evidence for each of these recommendations and offer practical advice about behavior-change techniques to help patients adopt brain-healthy behaviors.
... Fourth, while there is no direct evidence on the relationship between childhood friendship, social cohesion and cognitive aging, partly due to challenges in collecting life history data, our findings are supported by a strand of literature on later-life social cohesion, social networks and cognitive aging (Bassuk et al., 1999;Borenstein & Mortimer, 2016d, pp. 281-290;James et al., 2012). Emerging research on childhood social activities also corroborate our results (Chan et al., 2019). ...
Full-text available
We examine the long-term relationship between childhood circumstances and cognitive aging. In particular, we differentiate the level of cognitive deficit from the rate of cognitive decline. Applying a linear mixed-effect model to three waves of China Health and Retirement Longitudinal Surveys (CHARLS 2011, 2013, 2015) and matching cognitive outcomes to CHARLS Life History Survey (2014), we find that key domains of childhood circumstances, including family socioeconomic status (SES), neighborhood cohesion, friendship, and health conditions, are significantly associated with both the level of cognitive deficit and the rate of decline. In contrast, childhood neighborhood safety only affects the level of cognitive deficit. Childhood relationship with mother only affects the rate of cognitive decline. The effects of adverse childhood circumstances are generally larger on level of cognitive deficit than on rate of cognitive decline. Moreover, education plays a more important role in mediating the relationships compared to other later-life factors. These findings suggest that exposure to disadvantaged childhood circumstances can exacerbate cognitive deficit as well as cognitive decline over time, which may be partially ameliorated by educational attainment.
... Hence, future studies would be needed to address the association between quality of relationships and brain aging. Further, differences in brain structure related to social integration may be regional or subtle (James et al. 2012), which also seems to be the case for alcohol consumption (Topiwala et al. 2017). Even though accelerated brain aging has been shown in patients with alcoholism (Pfefferbaum et al. 1995), differences related to alcohol consumption in the normal population may not be as strong or only identifiable if several risk behaviors co-occur (Bittner et al. 2019). ...
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Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.
Background Recent evidence suggests that neurological health could be improved with the intervention of local green space. A few studies adopted cortical thickness, as an effective biomarker for neurodegenerative disorder, to investigate the association with residential greenness. However, they relied on limited data sources, definitions or applications to assess residential greenness. Our cross-sectional study assessed individual residential greenness using an alternative measure, which provides a more realistic definition of local impact and application based on the type of area, and investigated the association with cortical thickness. Methods The study population included 2542 subjects who participated in the medical check-up program at the Health Promotion Center of the Samsung Medical Center in Seoul, Korea, from 2008 to 2014. The cortical thickness was calculated by each of the four and global lobes from brain MRI. For greenness, we used the enhanced vegetation index (EVI) that detects canopy structural variation by adjusting background noise based on satellite imagery data. To assess individual exposure to residential greenness, we computed the maximum annual EVI before the date of a medical check-up and averaged it within 750 m from subjects' homes to represent an average walking distance. Finally, we assessed the association with cortical thickness by urban and non-urban populations using multiple linear regression adjusting for individual characteristics. Results The average global cortical thickness and EVI were 3.05 mm (standard deviation = 0.1 mm) and 0.31 (0.1), respectively. An interquartile range increase in EVI was associated with 11 μm (95% confidence interval = 3–20) and 9 μm (1–16) increases in cortical thickness of the parietal and occipital regions among the urban population. We did not find associations in non-urban subjects. Conclusions Our findings confirm the association between residential greenness and neurological health using alternative exposure assessments, indicating that high exposure to residential greenness can prevent neurological disorders.
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We examine the long-term relationship between childhood circumstances and cognitive aging. In particular, we differentiate the level of cognitive deficit from the rate of cognitive decline. Applying a linear mixed-effect model to three waves of China Health and Retirement Longitudinal Surveys (CHARLS 2011, 2013, 2015) and matching cognitive outcomes to CHARLS Life History Survey (2014), we find that key domains of childhood circumstances, including family socioeconomic status (SES), neighborhood cohesion, friendship and health conditions, are significantly associated with both the level of cognitive deficit and the rate of decline. In contrast, childhood neighborhood safety only affects the level of cognitive deficit. Childhood relationship with mother only affects the rate of cognitive decline. The effects of adverse childhood circumstances are generally larger on level of cognitive deficit than on rate of cognitive decline. Moreover, education plays a more important role in mediating the relationships compared to other later-life factors. These findings suggest that exposure to disadvantaged childhood circumstances can exacerbate cognitive deficit as well as cognitive decline over time, which may be partially ameliorated by educational attainment.
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We examined the association of social activity with cognitive decline in 1138 persons without dementia at baseline with a mean age of 79.6 (SD = 7.5) who were followed for up to 12 years (mean = 5.2; SD = 2.8). Using mixed models adjusted for age, sex, education, race, social network size, depression, chronic conditions, disability, neuroticism, extraversion, cognitive activity, and physical activity, more social activity was associated with less cognitive decline during average follow-up of 5.2 years (SD = 2.7). A one point increase in social activity score (range = 1-4.2; mean = 2.6; SD = 0.6) was associated with a 47% decrease in the rate of decline in global cognitive function (p < .001). The rate of global cognitive decline was reduced by an average of 70% in persons who were frequently socially active (score = 3.33, 90th percentile) compared to persons who were infrequently socially active (score = 1.83, 10th percentile). This association was similar across five domains of cognitive function. Sensitivity analyses revealed that individuals with the lowest levels of cognition or with mild cognitive impairment at baseline did not drive this relationship. These results confirm that more socially active older adults experience less cognitive decline in old age.
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White matter hyperintensity volume (WMHV), cerebral infarcts, and total brain volume (TBV) are associated with cognitive function, but few studies have examined these associations in the general population or whether they differ by race. To examine the association of WMHV, cerebral infarcts, and TBV with global cognition and cognition in 5 separate domains in a biracial population sample. A biracial community population of Chicago, Illinois. Cross-sectional population study. The study population comprised 575 participants from the Chicago Health and Aging Project (CHAP). Volumetric magnetic resonance imaging (MRI) measures of WMHV, TBV, and cerebral infarcts and detailed neuropsychological testing assessments of global cognition and 5 cognitive domains. Overall and among those without dementia, cognition was inversely associated with WMHV and number of infarcts but was positively associated with TBV. When all 3 measures were simultaneously added to the model, the association of global cognition with WMHV and TBV remained significant and unchanged but was no longer significant with infarcts. Among subjects without dementia, all 3 MRI measures were associated with performance in multiple cognitive domains, specifically perceptual speed. However, among subjects with dementia, only TBV was associated with cognition and performance in multiple cognitive systems. Race did not significantly modify any of these associations. In this biracial general population sample, the associations of MRI measures with cognition differed according to clinical status of subjects (stronger among subjects without dementia) and were not modified by race. These associations did not affect all cognitive domains equally but were more consistent with impairments in perceptual speed.
The recent availability of longitudinal data on the possible association of different lifestyles with dementia and Alzheimer's disease (AD) allow some preliminary conclusions on this topic. This review systematically analyses the published longitudinal studies exploring the effect of social network, physical leisure, and non-physical activity on cognition and dementia and then summarises the current evidence taking into account the limitations of the studies and the biological plausibility. For all three lifestyle components (social, mental, and physical), a beneficial effect on cognition and a protective effect against dementia are suggested. The three components seem to have common pathways, rather than specific mechanisms, which might converge within three major aetiological hypotheses for dementia and AD: the cognitive reserve hypothesis, the vascular hypothesis, and the stress hypothesis. Taking into account the accumulated evidence and the biological plausibility of these hypotheses, we conclude that an active and socially integrated lifestyle in late life protects against dementia and AD. Further research is necessary to better define the mechanisms of these associations and better delineate preventive and therapeutic strategies.
Social engagement, which is defined as the maintenance of many social connections and a high level of participation in social activities, has been thought to prevent cognitive decline in elderly persons. Associations between a socially engaged lifestyle and higher scores on memory and intelligence tests have been observed among community-dwelling older persons (1–5). Short-term interventions to foster social and intellectual engagement have enhanced cognition among nursing home residents (6) and patients with dementia (7). In animal studies (8), mature rodents exposed to complex social and inanimate environments showed better maze-learning ability than those in sparser surroundings. Social engagement challenges persons to communicate effectively and participate in complex interpersonal exchanges. Besides providing a dynamic environment that requires the mobilization of cognitive faculties, social engagement may also indicate a commitment to community and family and engender a health-promoting sense of purpose and fulfillment. Another putative benefit of social engagement is greater availability of emotional support from relatives and friends. Lack of such support can predict adverse health outcomes (9), but its influence on cognitive decline has not been examined.
Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.