Mindfulness Training Improves Cognition and Strengthens Intrinsic Connectivity Between the Hippocampus and Posteromedial Cortex in Healthy Older Adults

Abstract

Maintaining optimal cognitive functioning throughout the lifespan is a public health priority. Evaluation of cognitive outcomes following interventions to promote and preserve brain structure and function in older adults, and associated neural mechanisms, are therefore of critical importance. In this randomized controlled trial, we examined the behavioral and neural outcomes following mindfulness training ( n = 72), compared to a cognitive fitness program ( n = 74) in healthy, cognitively normal, older adults (65–80 years old). To assess cognitive functioning, we used the Preclinical Alzheimer Cognitive Composite (PACC), which combines measures of episodic memory, executive function, and global cognition. We hypothesized that mindfulness training would enhance cognition, increase intrinsic functional connectivity measured with magnetic resonance imaging (MRI) between the hippocampus and posteromedial cortex, as well as promote increased gray matter volume within those regions. Following the 8-week intervention, the mindfulness training group showed improved performance on the PACC, while the control group did not. Furthermore, following mindfulness training, greater improvement on the PACC was associated with a larger increase in intrinsic connectivity within the default mode network, particularly between the right hippocampus and posteromedial cortex and between the left hippocampus and lateral parietal cortex. The cognitive fitness training group did not show such effects. These findings demonstrate that mindfulness training improves cognitive performance in cognitively intact older individuals and strengthens connectivity within the default mode network, which is particularly vulnerable to aging affects.
Clinical Trial Registration: [ https://clinicaltrials.gov/ct2/show/NCT02628548 ], identifier [NCT02628548].

Full text

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ORIGINAL RESEARCH
published: 27 August 2021
doi: 10.3389/fnagi.2021.702796
Edited by:
Hanna Lu,
The Chinese University of Hong Kong,
China
Reviewed by:
Atsunobu Suzuki,
The University of Tokyo, Japan
Sindhuja T. Govindarajan,
University of Pennsylvania,
United States
*Correspondence:
Gunes Sevinc
guenessevinc@gmail.com
†These authors have contributed
equally to this work and share first
authorship
Received: 29 April 2021
Accepted: 09 August 2021
Published: 27 August 2021
Citation:
Sevinc G, Rusche J, Wong B,
Datta T, Kaufman R, Gutz SE,
Schneider M, Todorova N, Gaser C,
Thomalla G, Rentz D, Dickerson BD
and Lazar SW (2021) Mindfulness
Training Improves Cognition
and Strengthens Intrinsic Connectivity
Between the Hippocampus
and Posteromedial Cortex in Healthy
Older Adults.
Front. Aging Neurosci. 13:702796.
doi: 10.3389/fnagi.2021.702796
Mindfulness Training Improves
Cognition and Strengthens Intrinsic
Connectivity Between the
Hippocampus and Posteromedial
Cortex in Healthy Older Adults
Gunes Sevinc1*†, Johann Rusche1,2†, Bonnie Wong3, Tanya Datta1, Robert Kaufman1,
Sarah E. Gutz1,4 , Marissa Schneider1, Nevyana Todorova1,5, Christian Gaser6,
Götz Thomalla2, Dorene Rentz3,7 , Bradford D. Dickerson1,3 and Sara W. Lazar1
1Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States,
2Kopf- und Neurozentrum, Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,
3Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States,
4Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, United States,
5Department of Behavioral Neuroscience, College of Science, Northeastern University, Boston, MA, United States,
6Department of Psychiatry and Neurology, Jena University Hospital, Jena, Germany, 7Department of Neurology, Brigham
and Women’s Hospital, Harvard Medical School, Boston, MA, United States
Maintaining optimal cognitive functioning throughout the lifespan is a public health
priority. Evaluation of cognitive outcomes following interventions to promote and
preserve brain structure and function in older adults, and associated neural
mechanisms, are therefore of critical importance. In this randomized controlled
trial, we examined the behavioral and neural outcomes following mindfulness training
(n= 72), compared to a cognitive fitness program (n= 74) in healthy, cognitively normal,
older adults (65–80 years old). To assess cognitive functioning, we used the Preclinical
Alzheimer Cognitive Composite (PACC), which combines measures of episodic
memory, executive function, and global cognition. We hypothesized that mindfulness
training would enhance cognition, increase intrinsic functional connectivity measured
with magnetic resonance imaging (MRI) between the hippocampus and posteromedial
cortex, as well as promote increased gray matter volume within those regions. Following
the 8-week intervention, the mindfulness training group showed improved performance
on the PACC, while the control group did not. Furthermore, following mindfulness
training, greater improvement on the PACC was associated with a larger increase in
intrinsic connectivity within the default mode network, particularly between the right
hippocampus and posteromedial cortex and between the left hippocampus and lateral
parietal cortex. The cognitive fitness training group did not show such effects. These
findings demonstrate that mindfulness training improves cognitive performance in
cognitively intact older individuals and strengthens connectivity within the default mode
network, which is particularly vulnerable to aging affects.
Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/NCT02628548],
identifier [NCT02628548].
Keywords: aging, resting state – fMRI, mindfulness, cognitive composite, intervention
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INTRODUCTION
As maintaining optimal cognitive functioning throughout the
lifespan has become a public health priority, a number of
interventions that aim to slow or reverse normal age-related
decline have been proposed (Anguera et al., 2013;Rebok et al.,
2014;Banducci et al., 2017;Foster et al., 2019). Among those,
mindfulness training has been suggested to be an efficacious
method for enhancing cognitive functions that decline with age
(Gard et al., 2014;Fountain-Zaragoza and Prakash, 2017). Here,
in a randomized controlled longitudinal study, we investigated
cognitive outcomes and associated neural mechanisms following
an 8-week mindfulness meditation- based training program
compared to a “brain games” mental training program in
cognitively normal older adults. An enhanced understanding
of the mechanisms through which these interventions may
counteract age-related decline can provide novel insights
into training based cognitive improvements and enhance our
understanding of neural plasticity in aging.
Current interventions aim to help older adults maintain
optimal cognitive functioning either through explicit training
regimens that engage specific cognitive functions such as memory
(Requena et al., 2016), or use various techniques such as
transcranial direct current stimulation (Passow et al., 2017)
and neurofeedback (Reis et al., 2016;Jiang et al., 2017). Other
interventions aim to improve cognitive capacities indirectly
through exercise and diet programs (Colcombe and Kramer,
2003). In addition to limitations associated with their near-
and far-transferability (Shipstead et al., 2010;Melby-Lervåg and
Hulme, 2016;Grönholm-Nyman et al., 2017), these interventions
are also limited in terms of their availability to a broader
population of older adults.
More recently, mindfulness training have been proposed as an
efficacious intervention to enhance cognitive functions in healthy
older adults (Chiesa et al., 2011;Gard et al., 2014;Lao et al., 2016;
Fountain-Zaragoza and Prakash, 2017;Cásedas et al., 2020). In
line with enhanced attentional performance and preserved gray
matter volume in long term meditators (Pagnoni and Cekic,
2007), mindfulness meditation-based interventions have been
associated with improvements in attention, memory, executive
function, processing speed, as well as general cognition. However,
neural mechanisms associated with these improvements have
yet to be discovered. Mindfulness meditation emphasizes the
skill of meta-awareness to monitor distracting external or
internal events such as arising thoughts, in order to maintain
attention on the meditative object and prevent the mind from
wandering, enhancing meta-cognitive monitoring and meta-
cognitive control capacity (Schooler, 2002;Schooler et al., 2011).
By targeting these domains through mindfulness training, we
hypothesize training-specific, measurable cognitive performance
effects through mechanisms that are distinctive from other
cognitive training programs that use complex exogenous stimuli
to capture and maintain attention (Mozolic et al., 2011).
In the absence of external task-demands, the spontaneous
fluctuations in the blood-oxygen-level-dependent signal (BOLD)
have been shown to display temporally coherent activity patterns
within functional and anatomic systems of the brain (Biswal
et al., 1995;Greicius et al., 2003;Seeley et al., 2007). This
spontaneous during rest have already been associated with
individual variability in human behavior. In older adults,
particularly, decreases in cognition have been linked to decreases
in intrinsic connectivity of the default network. Neurocognitive
aging is associated with reduced deactivation of the default
network during task-positive states as well as with decreased
within-network connectivity during rest (Ferreira and Busatto,
2013;Madhyastha and Grabowski, 2013;Dennis and Thompson,
2014;Persson et al., 2014;Vidal-Piñeiro et al., 2014). Among
default mode network structures, posteromedial cortices that
are strongly functionally connected to the medial temporal
lobes, are selectively vulnerable to pathology (Sperling et al.,
2010). Critically, intrinsic connectivity between these regions,
particularly between the posteromedial cortex (PMC) and
hippocampus, has been associated with individual differences in
memory performance among cognitively intact older individuals
(Dickerson and Eichenbaum, 2010;Wang et al., 2010;Ferreira
et al., 2016). Morphological investigations of preserved cognitive
function in aging corroborate the critical role of these regions
in preserving cognitive functioning as well (Good et al., 2001;
Bakkour et al., 2013). The rate of cortical thinning in the
posteromedial cortex, along with other loci, is strongly associated
with the rate of cognitive decline (Dickerson and Wolk, 2012),
as well as with progression from mild cognitive impairment to
Alzheimer’s dementia (Chételat et al., 2005).
Mindfulness training-related increases in brain structure
and function partly overlap with the neural regions implicated
in age-related cognitive decline outlined above, in particular
the posterior cingulate cortex (PCC) and hippocampus.
Morphological investigations of mindfulness training have
documented increases in gray matter density in PCC and
hippocampus (Hölzel et al., 2011;Wells et al., 2013;Greenberg
et al., 2017). Alterations in hippocampal (Engström et al.,
2010;Yang et al., 2016) and PCC activity (Hasenkamp and
Barsalou, 2012;Garrison et al., 2013;Ellamil et al., 2016), as well
as increased connectivity between these regions during both
meditation and while resting have also been reported (Brewer
et al., 2011;Kilpatrick et al., 2011;Taylor et al., 2013;Wells
et al., 2013;Brewer and Garrison, 2014;Garrison et al., 2015;
Kral et al., 2019).
Although mindfulness training has been proposed as an
efficacious intervention for healthy aging, a mechanistic account
of mindfulness training alterations in cognition in older adults
is still lacking. Here we aimed to investigate neural mechanisms
associated with mindfulness training dependent changes in
cognition. To this end, we used a composite test battery that
combines measures of episodic memory, executive function,
and global cognition, that was developed to track normal age-
related cognitive decline as well as to predict early cognitive
changes in neurodegenerative diseases (Donohue et al., 2014;
Papp et al., 2017). Relying on the overlap in neural regions
implicated in age-related cognitive decline and mindfulness
training-related changes in neural functioning, we hypothesized
an association between increases in cognition and enhanced
intrinsic connectivity between the hippocampus and PMC. We
specifically hypothesized that a mindfulness-based intervention
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would improve cognitive function across multiple domains in
cognitively normal older adults relative to an active control
group, and that these improvements would be associated
with: (i) increased intrinsic connectivity between the PMC
and hippocampus; and (ii) increased gray matter volumes
in these regions.
MATERIALS AND METHODS
Recruitment, Randomization and
Blinding
Participants responded to advertising for a “Brain Training
Study” and were recruited via a direct mail campaign as well as
through various email list-servers. Following completion of all
baseline testing as specified below, the randomization module
within REDCap was used to randomize participants 1:1 into the
two training programs in permuted groups of six, by gender.
Study staff conducting subsequent testing visits were blind to
group status. Importantly, participants were told that both
training programs were effective for promoting cognition and
that the goal of the study was to determine differential neural
mechanisms, in order to minimize expectation and bias.
Participants
Potential participants were screened using the Telephone
Interview for Cognitive Status (TICS; Brandt et al., 1988)
to determine preliminary eligibility. Inclusion criteria were
65–80 years of age; right-handedness; ability to speak and
read English; stable medication usage for at least 30 days;
willingness to complete 40 min of homework per day during
the 8-week program, motivation to attend all eight classes,
presence in the area and availability during the follow-up
testing periods. Exclusion criteria included: any non-MRI
compatible metal in body; uncontrolled high blood pressure;
any cardiovascular disease; a past stroke, congestive heart failure
(subjects with well-controlled vascular risk factors, such as
treated hypertension or treated hyperlipidemia were included, as
were subjects with a history of cerebrovascular problems but no
persistent neurological deficits); uncontrolled diabetes or insulin-
treated diabetes [well-controlled Type II diabetes (glucose levels
<250) were included]; active hematological, renal, pulmonary,
endocrine, or hepatic disorders; history of neurological disease
or injury, including a history of seizures or significant head
trauma (i.e., extended loss of consciousness, bleeding in the
brain, Parkinson’s disease, stroke); received treatment for cancer
within the last 2 years; diagnosis of schizophrenia, posttraumatic
stress disorder, bipolar disorder, or psychotic disorder at any
point during lifetime; any axis I psychiatric disorder within
the last 12 months; any neurological or medical conditions
that would interfere with study procedures or confound results,
such as conditions that alter cerebral blood flow or metabolism;
use of psychotropic medications or medications with CNS
effects including cholinesterase inhibitors, memantine, and
benzodiazepines within 12 months prior to study [medications
taken on an occasional as needed basis (prn) were allowed, e.g.,
allergy relief]. Over the counter supplements, such as Gingko and
fish oil, were also allowed; any other medications as reviewed by
our team’s neurologist (BD) on a case-by-case basis. Individuals
were also excluded if they engaged in current regular practice
of meditation, yoga, tai chi, Feldenkrais or other mind-body
practices on more than six 30-min-long sessions within the last
6 months. Any other significant prior mind-body experience was
evaluated on a case-by-case basis by SL and decided upon based
on frequency, duration, recency, and type of mind-body practice,
with a general guideline of not more than 3 months of regular
practice in the last 5 years, or more than 12 months of practice in
their lifetime. Participants were also screened for physical activity
levels using the Godin-Shephard Leisure-time Physical Activity
questionnaire, sleep-related issues using Pittsburgh Sleep Quality
Index (Buysse et al., 1989).
While familiarity with leisure activities such as crossword
puzzles and sudoku, was not an exclusion criterion, participants
who had prior experience with a structured cognitive fitness
program such as Lumosity were excluded. Out of 74 participants
that were randomized to the or Cognitive Fitness Training
program, 45 had some prior experience (n= 27 with crossword
puzzles, n= 15 with sudoku, n= 6 word jumbles, and word search,
n= 23 with others such as solitaire, board games, or trivia games).
While 10 participants had experience with two types of puzzles,
none had experience with all four types trained in the course.
Similarly, out of the 72 participants who were randomized to the
Mindfulness Training, 28 had prior experience with yoga, tai-chi,
or mantra meditation, however, the frequency of their practice
was below our exclusion threshold.
Potential participants were invited to the laboratory,
consented, and then underwent a structured clinical interview
with our team’s neuropsychologist (BW) who performed a
cognitive and functional assessment to determine final eligibility.
Cognitively normal participants were determined on the basis
of both an absence of cognitive symptoms and absence of
impairment on cognitive testing (CDR Rating = 0; MMSE
27–30; normal performance on Trail-making Test, verbal
fluency measures based on age- and education matched norms).
Participants received the programs for free and were remunerated
up to $275 for their participation if they completed all testing
visits. Informed consent followed the guidelines of the MGH IRB.
Out of 1472 people who were screened, 146 eligible
participants were found eligible and randomized into either
Mindfulness Training (n= 72) or Cognitive Fitness Training
(n= 74) programs. Cognitive testing and neuroimaging were
conducted within a 3-week period before and after the
interventions (approximately a 3-month interval). The data
reported here are part of a longitudinal study with 2-year follow-
up. Only baseline and post-intervention performance in our
cognitive outcome measure are reported here. There was no
evidence of selective attrition. Please see CONSORT diagram for
additional information, including retention.
Cognitive Outcome Measure
Our primary cognitive outcome was the Alzheimer’s Disease
Cooperative Study Preclinical Alzheimer’s Cognitive Composite
(PACC; Donohue et al., 2014) which consists of: (1) the Total
Recall score from the Free and Cued Selective Reminding Test
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(FCSRT) (0–48) (Grober et al., 1988); (2) the Delayed Recall
score from the Logical Memory IIa subtest from the Wechsler
Memory Scale (0–25) (Wechsler, 1987); (3) the Digit Symbol
Substitution Test (DSST) from the Wechsler Adult Intelligence
Scale-Revised (0–93) (Wechsler, 1981); and the Mini Mental State
Exam (MMSE) total score (0–30) (Folstein et al., 1975). To reduce
practice effects, we administered alternate test versions at each
time-point. The cognitive composite score, PACC, is determined
from its components using an established normalization method
(Cutter et al., 1999). Each of the four component change scores
(post-pre) is divided by the baseline sample standard deviation of
that component, to form standardized z scores. These z scores are
summed using the following item weights as previously reported
(Donohue et al., 2014): 0.72 ×(FCSRT) +0.14 ×(Logical
Memory IIa) +0.12 ×(MMSE) +0.03 ×(DSST). The composite
score represents a standardized change score based on within-
participants alterations in cognition. According to Donohue et al.
(2014), the minimum treatment difference of 0.5 units is large
enough to suggest a benefit to the patients and also incorporates
a possible delay in later clinical deterioration.
Cognitive Training Programs
Mindfulness Training Program
The Mindfulness Training (MT) program is an 8-week program
that teaches mindfulness meditation exercises as a means to
enhance attention and memory. The program is derived from
Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990),
but with an emphasis on concentration and focus rather than
stress reduction. Weekly meetings lasted 1 h: 45 min meditation
practice and 15 min of check-in, practice instruction, and Q&A.
Participants were instructed to practice meditation at home for
45 min daily and were given guided audio recordings to facilitate
practice. Weekly mindfulness instruction consisted of: Weeks
1 and 2: breath meditation and body scan; Week 3: walking
meditation; Week 4: mental noting; Week 5: focus on the five
physical senses and sensations; Week 6: standing meditation;
Week 7: mindful eating and the five senses; Week 8: review all
techniques. Participants were allowed to practice any learned
technique in subsequent weeks if they desired. On average,
participants attended 7.05 of eight classes and practiced 4.01 h per
week at home. The program was taught by Greg Topakian, Ph.D.
who has 30 years of meditation practice including 20 weeks of
intensive retreat practice. He has 20 years of experience teaching
in academia as well as 6 years of experience teaching secular
mindfulness programs.
Cognitive Fitness Training Program
The Cognitive Fitness Training (CFT) program is an active
control condition matched to the MT program for amount of
class time and home practice. Like the MT program, class was
divided into 45 min of group puzzle solving and 15 min of check-
in, practice instruction, and Q&A. Weekly instruction consisted
of: Week 1: word search and crossword puzzles; Weeks 2 and 3:
Sudoku; Week 4: word jumbles; Weeks 5 and 6: KenKen; Weeks
7 and 8: review. Participants were given packets of puzzles to take
home and instructed to practice for 45 min each day. Importantly,
there was a range of difficulty available for each type of puzzle
during the first week it was introduced in order to accommodate
participants with different puzzle solving abilities. However, our
goal was to minimize the effectiveness of this program, and
so each participant continued to receive only puzzles at that
chosen difficulty level for the remainder of the program, to
limit development of novel strategies. On average, participants
attended 6.64 of eight classes and practiced 5.99 h per week at
home. The program was taught by Elisabeth Osgood-Campbell
who holds a master’s degree in education and has 13 years of
experience teaching in academic settings.
MRI Data Acquisition and Analysis
Data Acquisition Parameters
MRI imaging was conducted in a 3T scanner (Siemens Prisma)
with a 32-channel gradient head coil at the Athinoula A.
Martinos Center for Biomedical Imaging in Charlestown, MA,
United States. All subjects were scanned in the same scanner
at both time points, i.e., within 2 weeks before (pre-scan) and
within 2 weeks (post-scan) after participating in the 8-week
program (∼3-month interval). We acquired T1 structural MRI
images (sagittal MP-RAGE) for all subjects using the following
parameters: TA = 9:14; voxel size = 1.1 mm ×1.1 mm ×1.2 mm;
Rel.SNR = 1.00; slice oversampling = 0%; slices per slab = 176;
TR = 2300 ms; TE = 2.01 ms; field of view = 270 mm.
Subsequently, resting state functional magnetic resonance
imaging (rsfMRI) were acquired using a gradient-echo echo-
planar pulse sequence sensitive to the blood-oxygen-level-
dependent signal (BOLD) with the following parameters:
TR = 3000 ms; voxel size = 3.0 mm isotropic voxels;
Rel.SNR = 1.00; interleaved slice order, slice oversampling = 0%;
slice thickness = 3 mm; TE = 30 ms; Flip Angle = 85◦; TA = 6:12;
46 slices, field of view = 216 mm.
Structural Image Processing With Voxel Based
Morphometry (VBM)
Prior to preprocessing, the MP-RAGE data from 118 program
participants completing both scans were visually investigated
with regards to scanner artifacts as well as clinical abnormalities.
After preprocessing, the scans underwent an automated quality
check with the Computational Anatomy Toolbox’s (CAT12.6-
rc1; v1426; Structural Brain Mapping Group, Jena, Germany)
combining both measurements of noise and spatial resolution
to translate into an index of weighted overall image quality. The
resulting boxplot enabled a closer visual assessment of potential
outliers. Moreover, the covariance between all normalized
modulated images was assessed. Thereby we were able to ensure
sample homogeneity.
The preprocessing for the voxel-based morphometry was
conducted with CAT12’s longitudinal processing stream, which
was implemented in SPM12 (Wellcome Centre for Human
Neuroimaging, London, United Kingdom) running on MATLAB
(R2018b) (Mathworks Inc., Natick, MA, United States). In
this updated version, CAT12 is optimized to identify subtle
volumetric effects resulting from training over short time
periods. Default parameters were used unless specified otherwise.
Individual T1-weighted MRI images for both time-points were
processed by a series of steps, i.e., intra-subject alignment,
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bias correction, and segmentation. For the subsequent spatial
normalization, we used CAT12’s template for the high-
dimensional DARTEL registration with 1.5 mm (Ashburner
and Friston, 2001). This approach renders a higher sensitivity
for detecting regional differences (Bergouignan et al., 2009) as
well as an improved normalization power because of a better
inter-subject alignment (Yassa and Stark, 2009). As we were
interested to investigate the actual GM values locally and to detect
potential volumetric changes, images were modulated, i.e., each
tissue class image was multiplied by the Jacobian determinant
from the normalization matrix. Finally, images were smoothed
with an 8 mm FWHM isotropic Gaussian kernel via a SPM12
standard module. Smoothed images translate into an improved
normal distribution of the data, which is necessary to honor
the underlying assumption for parametric statistical comparisons
(Worsley et al., 1996). For the investigation of GM volume change
we extracted GM values and conducted statistical analysis of our
a priori seeds described below using SPSS version 25.
Seed-Based Connectivity Analyses
Resting state functional connectivity analyses were performed
using the CONN toolbox v.18b (Whitfield-Gabrieli and Nieto-
Castanon, 2012). Preprocessing consisted of realignment and
unwarping of functional images, slice timing correction and
motion correction. The functional images were resliced using
a voxel size of 2 mm ×2 mm ×2 mm and smoothed using
an 8-mm FWHM isotropic Gaussian kernel. ART was used
detect frames with fluctuations in global signal and motion
outliers. Intermediate level thresholds, which were set to reject
3% of the normative sample data, were used. The frames with
motion outliers that exceeded 0.9 mm or fluctuations in global
signal >5 standard were considered outliers. To address the
confounding effects of participant movement and physiological
noise the CompCor method (Behzadi et al., 2007) was used.
The structural images were segmented into cerebrospinal fluid
(CSF), white matter (WM), and GM. The principal components
related to the segmented CSF and WM were extracted and were
included as confound regressors in a first-level analysis along
with movement parameters. The data were linearly detrended
and band-pass filtered to 0.008–0.09 Hz, without regressing
the global signal. Quality assessment included inspection of the
sample in terms of maximum inter-scan motion, number of
valid scans per subject, and scan-to-scan change in global BOLD
signal and removal of outliers based on the aforementioned
criteria (n= 20).
For the determination of seeds, an initial seed located at the
posteromedial cortex seed/or in posterior cingulate/retrosplenial
cortex (MNI coordinates x=−1, y=−52, z= 26) with an 8 mm
radius was selected based on previous literature that investigated
large-scale networks in older adults (Andrews-Hanna et al.,
2007). The hippocampal seeds were determined based on the
pattern of correlations at baseline for the whole sample using
posterior cingulate/retrosplenial cortex (pC/rsp) seed. After a
voxel level correction at p<0.001, and a cluster level at
p-FWE <0.05, spherical ROIs with a radius of 8 mm were defined
around the following peak coordinates within the hippocampi
(hippocampus/R 30 −16 −14; hippocampus/L −26 −28 −14).
In order to assess group differences in alterations in intrinsic
connectivity between our a priori seeds, we first examined
connectivity estimates between a priori hippocampus and
posterior cingulate/retrosplenial cortex (pC/rsp) seeds at each
time point. In order to further delineate within-group changes
in intrinsic connectivity in relation to changes in cognition,
follow-up gPPI analyses were conducted for each group. For
each group, a generalized psychophysiological interaction (gPPI)
analysis computed the level of changes in functional connectivity
strength between hippocampal seeds (R/L) and every voxel in the
brain (post-pre), covarying with changes in cognition (PACC).
Statistical Analysis Methods for
Behavioral and Neural Outcome
Measures
To assess within group differences for PACC, a one-sample
t-test was conducted for each group, where group means were
compared to a mean equal to zero, indicating no change in
PACC. To assess differences in PACC between mindfulness-
based and cognitive fitness trainings, an independent samples
t-test was used. The connectivity estimates reflect the change
in connectivity associated with training-dependent increases
in cognition. Group differences in changes in connectivity
estimates between a priori hippocampus and posterior
cingulate/retrosplenial cortex (pC/rsp) seeds were evaluated
using a repeated measures ANOVA. To assess changes in
hippocampal connectivity strength covarying with changes in
cognition (PACC), separate gPPI models were used for the right
and the left hippocampal connectivity. For each participant
(within-participants level), whole brain time series data were
regressed onto the ROI signal to generate connectivity maps
at each time point (baseline and post-intervention). Post
intervention bivariate regression coefficient maps were then
subtracted from baseline maps to create a map of whole-brain
connectivity changes with each hippocampal seed for each
participant. At the second (between-participants) level, these
change maps were then regressed onto PACC scores to create
a map of regions whose connectivity change significantly
correlated with PACC. To explore changes related to MT,
first the gPPI analysis was run on participants from the MT
group, followed by the CFT group alone. Both gPPI statistics
were evaluated via SPM 8 using a voxel level threshold at
p<0.001, and a cluster level threshold at p-FWE <0.05 for
multiple comparisons. Bivariate regression coefficients were then
extracted from all participants at each time-point to allow for
comparison of MT changes relative to the CFT group.
RESULTS
Cognitive Outcomes
In the MT group, PACC scores increased after the intervention
compared to baseline [0.21 mean increase ±0.68 standard
deviations (SD); t(60) = 2.44, p= 0.018, CI (0.04–0.39), Cohen’s
d0.31]. In the CFT group, PACC scores did not increase relative
to baseline following the intervention [0.10 mean increase ±0.64
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FIGURE 1 | Cognitive improvement relative to baseline performance. PACC scores calculated as change from baseline following the interventions for each group.
Mindfulness training resulted in a significant within-group increase in cognition (*p<0.05), while cognitive fitness training did not.
SD; t(64) = 1.30, p= 0.20, CI (−0.06 to 0.26), Cohen’s d0.16].
Despite these findings, the between-group comparison was not
statistically significant [t(124) = 0.942, p= 0.348, CI (−0.121,
0.342), neuroimaging sample t(95) = 1.235, p= 0.220, CI (−0.103,
0.442), Figure 1].
Baseline characteristics of the whole sample are presented in
Table 1. Performance on PACC as well as performance on each
cognitive test at each time point are presented in Table 2. There
were no differences between groups in FCSRT [t(124) = 0.142,
p= 0.888], in Logical Memory IIa [t(124) = −0.040, p= 0.968],
in MMSE [t(124) = 0.946, p= 0.346], or in DSST [t(124) = 1.291,
p= 0.199] at baseline. The significant improvement in the PACC
composite score for the MT group was driven by primarily by
an increase in the FCSRT total recall that was not seen in the
control group. Both groups improved on LMIIa delayed recall
performance and showed slight improvements on digit symbol
substitution test. The MMSE was uninformative in this study
because many participants performed at ceiling at baseline.
There was no significant difference between groups in terms
of their physical activity [t(130) = 0.890, p= 0.375], or sleep
levels [t(124) = 0.468, p= 0.641] at baseline either. The changes
in PSQI scores from baseline to post-testing did not differ
between groups [F(1,126) = 1.194, p= 0.277, η2= 0.01].
Mindfulness Training group had the following PSQI scores at
baseline (4.83 ±3.03), and at post (4.66 ±2.92), while the
Cognitive Fitness Training group had the following PSQI scores
at baseline (4.72 ±3.06), and at post (4.75 ±2.95). The changes
in exercise scores from baseline to post-testing did not differ
between groups either [F(1,114) = 2.748, p= 0.100, η2= 0.24].
Mindfulness Training group had the following Godin exercise
scores at baseline (34.89 ±20.10), and at post (35.75 ±2157),
while the Cognitive Fitness Training group had the following
scores at baseline (38.64 ±27.58), and at post (49.72 ±35.73).
Mindfulness Training Is Associated With
Increased Intrinsic Connectivity Between
the Right Hippocampus and
Posteromedial Cortex
To assess group differences in alterations in intrinsic
connectivity between our a priori seeds, we first examined
connectivity estimates between a priori hippocampus and
posterior cingulate/retrosplenial cortex (pC/rsp) seeds at
each time point. An investigation of group differences in
TABLE 1 | Baseline characteristics of study participants.
Mindfulness
training
Cognitive
fitness
training
Statistical
test value
pCohen’s d
Sample size 70 75
Age (years) 70.2 ±4.1
(n= 70)
71.0 ±4.3
(n= 75)
t=−1.10 0.27 0.19
Gender (% female) 55.7 (n= 70) 53.3 (n= 75) χ(1) = 0.08 0.77
Education (years) 16.7 ±1.8
(n= 69)
16.7 ±1.9
(n= 74)
t= 0.12 0.91 0.00
Education (ISCED
level)
6.5 ±1.0
(n= 69)
6.5 ±1.1
(n= 74)
t= 0.10 0.92 0.00
Numbers denote mean ±standard deviation. p indicates the significance of the
group differences on Students’ t or chi-square test.
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changes connectivity estimates between pC/rsp and left
hippocampus seed [F(1,95) = 0.048, p= 0.827, η2= 0.001], and
between pC/rsp and right hippocampus seed [F(1,95) = 0.011,
p= 0.916, η2= 0.000] did not reveal any differences between
groups over time.
Next, in order to delineate mindfulness training dependent
changes in hippocampal connectivity strength that covary with
changes in cognition, we conducted a whole brain gPPI analysis
(baseline vs. post-intervention) using PACC scores as regressor
and right hippocampus as seed region. This analysis resulted
in a significant cluster at right precuneus for the mindfulness
training group [MNI coordinates +6−52 +54, cluster size
(k) = 74, p-FWE = 0.037, Figure 2A]. Mindfulness-training
dependent improvements in cognitive composite scores were
associated with increases in intrinsic connectivity between the
right hippocampus and right precuneus (r= 0.526, p<0.001,
Figure 2B). A parallel whole brain gPPI analysis (baseline
vs. post-intervention) using PACC scores as regressor and
right hippocampus as seed region in the CFT group did
not yield any results. In order to compare two groups,
connectivity estimates between the right hippocampus and
TABLE 2 | Cognitive outcome measures.
Mindfulness training
Pre Post t p Cohen’s d
PACC n= 70 (n= 61) n= 61
Digit symbol 51.8 ±9.7
(51.3 ±9.3)
52.6 ±9.1 −1.66 0.10 0.09
MMSE 29.1 ±1.0
(26.1 ±1.1)
29.0 ±1.3 0.25 0.80 0.09
FCSRT total
recall
30.8 ±5.8
(31.2 ±5.7)
32.3 ±4.8 −1.93 0.06 0.28
LMIIa delayed
recall
12.2 ±4.2
(12.3 ±4.2)
13.9 ±4.1 −2.70 0.009 0.41
Total PACC
change
(n= 61)
0.21 ±0.68
Cognitive fitness training
Pre Post t p Cohen’s d
PACC n= 75 (n= 65) n= 65
Digit symbol 49.0 ±11.0
(48.9 ±11.6)
50.3 ±11.1 −1.83 0.07 0.12
MMSE 29.0 ±1.0
(28.9 ±1.0)
28.8 ±1.0 1.14 0.26 0.2
Free recall 31.1 ±5.1
(31.1 ±5.2)
31.4 ±4.9 −0.35 0.73 0.06
Delayed recall 12.5 ±3.3
(12.3 ±3.3)
14.7 ±4.0 −5.12 <0.001 0.6
Total PACC
change
(n= 65)
0.10 ±0.64
Numbers denote mean ±standard deviation for each cognitive measure, and
PACC; p values pertain to paired t-tests between participants with both baseline
and follow-up measures.
the cluster in the precuneus were extracted. While there was
no association between improvements in cognitive composite
scores and increases in intrinsic connectivity between the right
hippocampus and right precuneus in the CFT group (r=−0.023,
p= 0.876, Figure 2B), a test for between-group differences was
not significant [F(1,95) = 0.264, p= 0.609, η2= 0.003].
Mindfulness Training Is Associated With
Increased Intrinsic Connectivity Between
the Left Hippocampus and the Right
Angular Gyrus
A whole brain gPPI analysis (baseline vs. post-intervention) using
PACC scores as regressor and left hippocampus as seed region
resulted in a significant cluster in the right angular gyrus for
the mindfulness training group [MNI coordinates +62 −48 +16,
cluster size (k) = 116, p-FWE = 0.003, Figure 2C]. Mindfulness-
training dependent improvements in cognitive composite scores
were associated with increases in intrinsic connectivity between
the left hippocampus and the right angular gyrus (r= 0.538,
p= 0.000, Figure 2D). A parallel whole brain gPPI analysis
(baseline vs. post-intervention) using PACC scores as regressor
and left hippocampus as seed region in the CFT group did not
yield any results. In order to compare two groups, connectivity
estimates between the left hippocampus and angular gyrus
were extracted as well. While there was no association between
improvements in cognitive composite scores and increases in
intrinsic connectivity between the left hippocampus and angular
gyrus (r= 0.232, p= 0.108, Figure 2D) for the CFT group,
and a test for between-group differences was not significant
[F(1,95) = 1.647, p= 0.203, η2= 0.017].
Our hypotheses about changes in gray matter volume were not
supported. There was no main effect of time nor any significant
within-group changes within our ROIs for the mindfulness group
(all p>0.48). There was a main effect of time in the right
hippocampus for the CFT group which did not survive multiple
comparisons correction. Moreover, in opposition to our a priori
hypothesis, we were not able to identify any significant results
when correlating GMV change values with PACC change scores.
DISCUSSION
In the present study, we performed a randomized controlled trial
to test the hypothesis that mindfulness training can maintain or
improve cognitive function in healthy older adults, and we used
functional and structural MRI to investigate the neural basis of
cognitive outcome. We found that an 8-week mindfulness-based
training program improved cognition as assessed by Preclinical
Alzheimer’s Cognitive Composite (PACC) in cognitively normal
older adults, and that these improvements were associated with
increased intrinsic connectivity within the default mode network,
particularly between the right hippocampus and precuneus and
between the left hippocampus and right lateral parietal cortex.
Although the active control group did not show these effects, we
were not able to demonstrate a statistically significant between-
group difference in the primary cognitive outcome measure,
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FIGURE 2 | Training-dependent changes in hippocampal connectivity strength that covary with changes in cognition. (A) A whole brain gPPI analysis of changes in
functional connectivity (baseline vs. post-intervention) using change in PACC scores as regressor and right hippocampus as seed resulted in a significant cluster at
the right precuneus for the mindfulness training group. Networks are based on the Yeo seven-network parcellation (Yeo et al., 2011) and are represented by the
following colors: violet: visual, blue: somato-motor, green: dorsal attention, pink: ventral attention, cream: limbic, orange: fronto-parietal, and red: default
network. (B) Mindfulness-training-dependent improvements in PACC cognitive composite scores correlated with increases in intrinsic connectivity between the right
hippocampus and the right precuneus, while the Cognitive Fitness Training group showed no association. The connectivity estimates reflect the change in
connectivity strength associated with training-dependent increases in cognition, and were plotted using SPSS v.24 (Chart Editor). The fitted regression line reflects
the best estimate of the connectivity between the hippocampus and the precuneus in B.(C) A whole brain gPPI analysis of the changes in functional connectivity
(baseline vs. post-intervention) using change in PACC scores as regressor and left hippocampus as seed resulted in a significant cluster at the right angular gyrus for
the mindfulness training group. Networks are based on the Yeo seven-network parcellation (Yeo et al., 2011) and are represented by the following colors: violet:
visual, blue: somato-motor, green: dorsal attention, pink: ventral attention, cream: limbic, orange: fronto-parietal, and red: default
network. (D) Mindfulness-training-dependent improvements in cognitive composite scores correlated with increases in intrinsic connectivity between the left
hippocampus and the right angular gyrus, while the Cognitive Fitness Training group showed no association. The connectivity estimates reflect the change in
connectivity strength associated with training-dependent increases in cognition, and were plotted using SPSS v.24 (Chart Editor). The fitted regression line reflects
the best estimate of the connectivity between the left hippocampus and the right angular gyrus in C.
likely because the effect size of the mindfulness program was
small over this relatively short period of time, control training
program was more active than anticipated and/or due to overlaps
between the two programs in terms of their utilization of
attention and attentional control mechanisms. Nevertheless,
these findings suggest that additional longer-term studies of the
potential benefits of mindfulness training should be investigated
as an activity that could potentially contribute to the prevention
of age-related cognitive decline.
The enhanced cognition scores following mindfulness training
can be attributed primarily to improved episodic memory
performance on both the Free and Cued Selective Reminding
Test and the Logical Memory II Delayed Recall Test (Wechsler,
1987;Grober et al., 2008). These findings are consistent with
several reviews and meta-analyses which reported moderate
effects of mindfulness training on memory specificity (Chiesa
et al., 2011;Gard et al., 2014;Lao et al., 2016;Fountain-Zaragoza
and Prakash, 2017). The “brain games” practiced by the control
group included crossword puzzles and word jumbles, both of
which engage semantic memory (Pillai et al., 2011). Thus the lack
of between group differences is likely due to the fact that engaging
in meaningful mental stimulation and intellectual activity can
improve performance on tasks that tap into the same cognitive
domain that is trained (Aguirre et al., 2013). Importantly, while
neither group exhibited significant levels of improvement in free
recall, while the mindfulness training exhibited an improvement
that approached significance. Here it is important to note the
sensitivity of episodic memory to age-related decline (Donohue
et al., 2014). Therefore, an improvement in this ability may
be deemed to have potential clinical significance, especially in
delaying age-dependent memory decline. Compared to other
training programs in healthy older adults that found little to no
improvements in memory (Gross et al., 2012), current findings of
training-dependent improvements in the PACC, particularly in
free recall, further support the use of mindfulness training as an
activity to promote successful cognitive aging.
Growing evidence suggests that age-related cognitive decline
is associated with changes in functional connectivity within
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and between large-scale brain networks (Andrews-Hanna et al.,
2007;Ferreira and Busatto, 2013;Damoiseaux, 2017). Relative
to younger adults, cognitively intact older adults show reduced
functional connectivity within the default mode network at rest
(Ward et al., 2015;Damoiseaux, 2017;Staffaroni et al., 2018),
as well as less pronounced deactivations during cognitive tasks
(Grady et al., 2006;Persson et al., 2014;Spreng et al., 2016).
Decreased resting state connectivity between the hippocampus
and precuneus/posterior cingulate have been implicated in
typical age-related cognitive decline (Wang et al., 2010;Bernard
et al., 2015;Li et al., 2020). Although studies with cross-sectional
populations suggest that there is a low share for the overall
connectivity strength of the default network in explaining the
age-related variance across various cognitive domains (Hedden
et al., 2016), here, in a large sample of cognitively normal older
adults, we report an association between improved cognition
and training-dependent increases in the intrinsic connectivity
between the right hippocampus and precuneus and between the
left hippocampus and right lateral parietal cortex.
The increase in functional connectivity between the
posteromedial cortex and hippocampus in the mindfulness
training group was strongly associated with increases in episodic
memory. This finding supports our initial hypothesis that
mindfulness training may improve cognition in part through
changed connectivity within the default mode network. Such an
interpretation is also congruent with reports of a hippocampal-
parietal network that is associated with episodic memory
retrieval (Vincent et al., 2006), reports of positive association
between default network connectivity and episodic memory
(Huo et al., 2018), reports of an association between greater
within network functional connectivity and cognitive status
in healthy older adults (Sullivan et al., 2019), as well as with
reports of mindfulness training dependent connectivity increases
within the default mode network (Brewer et al., 2011;Taylor
et al., 2013;Wells et al., 2013). The posterior parietal cortex
and the precuneus are among the regions most frequently
activated during both successful memory formation and episodic
memory retrieval (Buckner et al., 2008;Spreng et al., 2009),
and are particularly susceptible to neuropathological changes
associated with aging and Alzheimer’s Dementia (Buckner,
2004). Thus, present findings help substantiate the idea that
enhanced intrinsic connectivity between the hippocampus and
posteromedial cortex may represent one neural mechanism
by which mindfulness training promotes memory function in
healthy older adults.
We also identified a mindfulness training related increase
in coordinated neural activity between left hippocampus and
right angular gyrus. This finding is in accordance with our prior
findings of mindfulness training dependent reorganization of
hippocampal-cortical networks during retrieval of extinguished
fear memories (Sevinc et al., 2019, 2020). Both the precuneus
and the angular gyrus are part of the dorsal medial subsystem
of the default mode network, that have been associated
with metacognitive reflection (D’Argembeau et al., 2014). The
dorsomedial and the medial temporal subsystems are closely
linked, and both have been shown to be recruited during memory
tasks (Andrews-Hanna et al., 2010). Critically, the angular gyrus
is part of the ventral parietal cortex that is thought to direct
attention to memory contents (Cabeza et al., 2008;Ciaramelli
et al., 2008). Although future task-based studies are needed, the
results suggest that mindfulness-training based increases in the
ability to direct attention to memory contents may be one of
the mechanisms through which mindfulness training increases
memory performance.
While the design of the present study precludes determining
whether the observed memory enhancements resulted from
improved encoding or retrieval, consistent with research
documenting the relation between mindfulness and attention,
we had originally hypothesized that mindfulness training-
dependent enhanced awareness of present moment experience
would contribute to memory encoding (see Chiesa et al., 2011;
Tang et al., 2015, for reviews). The present data suggest that
mindfulness training related enhanced connectivity within the
default mode network may contribute to improved memory via
enhanced encoding or enhanced retrieval mechanisms. These
findings are also in agreement with reports of meditation practice
moderating aging-related decrements in measures of sustained
attention (Zanesco et al., 2018). Conducting more nuanced
memory tasks within the MRI scanner will be required to
precisely define the impact of mindfulness training on each
component of memory encoding and retrieval.
The PACC cognitive composite utilized in the study has
been designed to be sensitive to cognitive changes in older
adults, especially to the earliest signs of cognitive decline in
Alzheimer’s disease (AD; Donohue et al., 2014). Test scores
that constitute the composite scores have long been used as
primary markers of disease progression as well as measure of
treatment effects (Amieva et al., 2008, 2019). PACC performance
has reliably characterized and quantified the risk for Alzheimer-
related cognitive decline among cognitively normal individuals
with elevated levels of brain amyloid (Donohue et al., 2017).
Consequently, a low score on the Free Recall measure has been
suggested as a core neuropsychological marker of prodromal AD
(Auriacombe et al., 2010). Similarly, alterations in connectivity
between the precuneus/posterior cingulate and the hippocampus
during rest have been implicated in MCI and AD patients
(Wang et al., 2006;Sperling et al., 2010;Çiftçi, 2011;Vannini
et al., 2013). Thus, training dependent increases in precuneus-
hippocampal connectivity seen in the current study suggest that
mindfulness-training may also be one of the mechanisms through
which mindfulness training improves memory in individuals
with mild cognitive impairment (Wells et al., 2013;Yang et al.,
2016;Wong et al., 2017), and also contribute to discussions
around brain regions associated with cognitive reserve in aging
(Solé-Padullés et al., 2009).
An important strength of the study was the use of a
“stripped down” mindfulness program which focused exclusively
on teaching formal mindfulness meditation exercises and did
not contain any psycho-education, or cognitive or behavioral
therapy elements. Further, we used an engaging, credible, active
control condition which was portrayed to the participants as
being equally efficacious as the mindfulness program. Together,
these study design elements allowed us to identify effects that
were specifically attributable to mindfulness practice rather than
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Sevinc et al. Mindfulness Training and Successful Cognitive Aging
to generic effects of participating in a group activity, or to
other therapeutic elements that are usually included in clinical
mindfulness based interventions (Kabat-Zinn, 1990;Segal and
Williams, 2002). As they were no differences between groups
in terms of changes in physical activity or sleep quality over
time, it is unlikely that the reported changes are due to these
potential mediators. Other strengths of the study include the large
sample size, blinded outcome of assessors, highly experienced
teachers, and excellent participant compliance and retention, all
of which have been major issues for many prior mindfulness
studies (Chiesa et al., 2011;Tang et al., 2015;van der Velden et al.,
2015;Lao et al., 2016;Van Dam et al., 2018).
The primary limitation of the study is that despite our efforts
to match the groups on amount of time spent practicing at
home, the CFT group practiced considerably more than what
was prescribed, while the MT group practiced slightly less than
prescribed. Therefore, this study bears the risks of type II errors,
i.e., omitting potential group-by-time effects undermined by
differential adherence to the study design. However, the within-
group analyses help circumvent this issue, as do the differential
correlations between brain and cognitive changes. Furthermore,
some of the participants in the CFT group were already familiar
with the training materials used in the program, which could
contribute to smaller effect sizes in the CFT group. Thus, null
training effect in the CFT group may be partially explained
by their familiarity with some of the training materials prior
to enrollment. As such, major limitation of the study is the
lack of a significant between-group difference. Future research
is needed to assess dissociable cognitive outcomes using more
specified attentional measures and associated neural mechanisms
of action. Future research may also assess whether the neural
changes and cognitive improvements reported in this study are
affected from confounding factors such as age, sex, education,
whether these gains also translate into tangible gains in everyday
life activities, whether the positive effects observed will be
maintained over a longer period of time, and to what degree
these interventions can delay the onset of various forms of
cognitive decline.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Mass General Brigham Human Research
Committee. The patients/participants provided their written
informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
SL and BD contributed to the conception and design of the study.
GS, TD, RK, SG, MS, and NT carried out the data collection.
GS and JR performed the statistical analysis and wrote the first
draft of the manuscript. SL oversaw all the data collection and
analysis. CG, GT, DR, and BD oversaw the data analysis. DR, BD,
and SL contributed to the interpretation of the results. All authors
provided critical feedback, discussed the results, and commented
on and approved the submitted manuscript.
FUNDING
We thank the National Institute on Aging for providing primary
funding for this study (R01 AG048351 to SL and BD). This
research was carried out at the Athinoula A. Martinos Center
for Biomedical Imaging at MGH, using resources provided
by the Center for Functional Neuroimaging Technologies,
P41EB015896, a P41 Biotechnology Resource Grant supported by
the National Institute of Biomedical Imaging and Bioengineering
(NIBIB), and the Neuroimaging Analysis Center, P41EB015902,
a P41 supported by NIBIB. This work also involved the
use of instrumentation supported by the National Institutes
of Health (NIH) Shared Instrumentation Grant Program;
specifically, S10RR017208-01A1, S10RR026666, S10RR022976,
S10RR019933, S10RR023043, and S10RR023401.
ACKNOWLEDGMENTS
We would like to thank Clinical Research Coordinators SG,
MS, Erin Mulvihill, Burak Cindik, and numerous undergraduate
students for their assistance. We would also like to thank our
participants for their time and effort, Greg Topakian for teaching
the mindfulness program, and Elizabeth Osgood-Campbell for
teaching the cognitive fitness training program. Parts of this work
were prepared in the context of JR’s dissertation at the Faculty of
Medicine, University of Hamburg.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnagi.
2021.702796/full#supplementary-material
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