NeuroImage 134 (2016) 508–513

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NeuroImage
journal homepage: www.elsevier.com/locate/ynimg

Estimating brain age using high-resolution pattern recognition: Younger
brains in long-term meditation practitioners
Eileen Luders a,b,⁎,1, Nicolas Cherbuin b,1, Christian Gaser c,da

Department of Neurology, UCLA School of Medicine, Los Angeles, USA
Centre for Research on Ageing Health and Wellbeing, Australian National University, Canberra, Australia
Department of Psychiatry, Jena University Hospital, Jena, GermanydDepartment of Neurology, Jena University Hospital, Jena, Germanybca r t i c l e

i n f oArticle history:
Received 9 October 2015
Revised 24 March 2016
Accepted 4 April 2016
Available online 11 April 2016
Keywords:
Aging
Brain
Gray matter
Meditation
Mindfulness
MRI

a b s t r a c t
Normal aging is known to be accompanied by loss of brain substance. The present study was designed to examine
whether the practice of meditation is associated with a reduced brain age. Speciﬁc focus was directed at age ﬁfty
and beyond, as mid-life is a time when aging processes are known to become more prominent. We applied arecently developed machine learning algorithm trained to identify anatomical correlates of age in the brain translating those into one single score: the BrainAGE index (in years). Using this validated approach based on highdimensional pattern recognition, we re-analyzed a large sample of 50 long-term meditators and 50 control
subjects estimating and comparing their brain ages. We observed that, at age ﬁfty, brains of meditators were estimated to be 7.5 years younger than those of controls. In addition, we examined if the brain age estimates changewith increasing age. While brain age estimates varied only little in controls, signiﬁcant changes were detected inmeditators: for every additional year over ﬁfty, meditators' brains were estimated to be an additional 1 monthand 22 days younger than their chronological age. Altogether, these ﬁndings seem to suggest that meditation
is beneﬁcial for brain preservation, effectively protecting against age-related atrophy with a consistently slower
rate of brain aging throughout life.
© 2016 Elsevier Inc. All rights reserved.

1. Introduction
Meditation is attracting increasing interest in relation to health andwellbeing but its biological effects are not well understood. Althoughpublication bias and methodological limitations are strong concerns inthis emerging ﬁeld, meditation has been shown to induce increases inbrain tissue, even after relatively short periods of time, such as weeks
or months (Fox et al., 2014). Complementing these short-term effects,
brains of long-term meditators have been reported to be structurally
different, with thicker, better connected, and more complex cortical sections, larger volumes, areas and dimensions of speciﬁc brain regions, aswell as more local brain tissue than in brains of healthy controls (Luders
et al., 2013a, 2015; Fox et al., 2014).
Since normal aging is known to be accompanied by loss of brain substance (Raz et al., 2010; Pfefferbaum et al., 2013), the question arises as
to whether meditation may have a protective effect against age-related
brain atrophy, where effects might accumulate over time and become
evident especially in later years of life. The present study was designed
to examine whether the practice of meditation manifests as a seemingly⁎ Corresponding author at: Department of Neurology, UCLA School of Medicine, 635
Charles E Young Drive South, Suite 225, Los Angeles, CA 90095-7334, USA.
E-mail address: eileen.luders@ucla.edu (E. Luders).
1
Both authors contributed equally.

http://dx.doi.org/10.1016/j.neuroimage.2016.04.007
1053-8119/© 2016 Elsevier Inc. All rights reserved.

reduced brain age in meditators. Speciﬁc focus was directed at age ﬁfty
and beyond, as mid-life is a time when aging processes are known to become more salient as well as more functionally signiﬁcant (Fraser et al.,2015).
We utilized a recently developed and validated high-dimensional
pattern recognition approach which allows estimating, automaticallyand objectively, the age of any given brain based on a single T1weighted brain image (Franke et al., 2010, 2012). Importantly, in people
aged 19–86 years, this method has been shown to predict brain age witha mean error of as little as 4.98 years (Franke et al., 2010). Moreover, as
also evaluated previously (Franke et al., 2010), the 95% conﬁdence interval for the prediction of brain age is stable across the entire age
range, even in older adults (e.g., age [mean ± SD] = 20 ± 11.6 years
vs. age [mean ± SD] = 80 ± 11.7 years). This is especially relevant
for our current sample, which is composed of 100 subjects (50 meditators/50 controls) ranging in age between 24 and 77 years.
The applied method effectively translates the complex, multidimensional aging pattern across the whole brain into one single score: thebrain age (BrainAGE) index. The polarity of the index indicates if brains
appear younger (negative score) or older (positive score) than theirchronological age, and the numeric value speciﬁes the magnitude ofthe difference (in years) between estimated age and chronologicalage. For example, estimating the brain ages of individuals with mild
Alzheimer's disease in a previous study revealed a mean BrainAGE

E. Luders et al. / NeuroImage 134 (2016) 508–513

index of + 10 (Franke et al., 2010). These striking ﬁndings suggested
signiﬁcantly accelerated brain aging in Alzheimer's patients compared
to normal aging in healthy controls (Franke et al., 2010). In contrast,
for the current study focusing on the potential brain-preserving effectof long-term meditation, we predicted decelerated brain aging in meditators compared to normal aging in healthy controls. With respect tothe magnitude of the effect, the deviation from ‘normality’ may range
between a few days to several years.
2. Methods
2.1. Subjects
Note that this was a re-analysis of data overlapping, fully or in part,with those used in prior studies (Luders et al., 2009, 2011, 2012a,b,
2013a,b, 2014, 2015; Kurth et al., 2015a,b). The study sample included
50 meditation practitioners (28 men, 22 women) and 50 control subjects (28 men, 22 women). Meditators and controls were closely
matched for chronological age, ranging between 24 and 77 years witha mean age of 51.4 years in both groups (SD = 12.8 years in meditators;
SD = 11.8 years in controls). Meditators were recruited from various
venues in the greater Los Angeles area and had close to twenty yearsof meditation experience on average (mean: 19.8 years; SD =11.4 years; range: 4–46 years). A detailed overview with respect toeach subject's individual practice has been previously provided(Luders et al., 2012a). Brain scans for the control subjects were obtained
from the ICBM database of normal adults (http://www.loni.usc.edu/
ICBM/Databases/). All subjects gave their informed consent in accordance with the policies and procedures of UCLA's Institutional Review
Board.
2.2. Image Acquisition509

2.4. The BrainAGE Estimation Framework
The BrainAGE framework uses relevance vector regression (RVR), a
machine learning approach based on pattern recognition. It has beensuccessfully applied in a range of studies (Franke et al., 2010, 2012,2013; Gaser et al., 2013; Franke et al., 2014, 2015) and was originally developed to model the healthy aging patterns of the brain, as detailed
elsewhere (Franke et al., 2010, 2012). Brieﬂy, the initial model has
been trained using the preprocessed gray matter segments in a sample
of more than 650 subjects, aged between 19 and 86 years. Informationon the most important brain regions used by the RVR for estimating
the individual brain ages has been provided in the accompanying
methods paper (Franke et al., 2010). When applied to new brainscans, speciﬁcally the preprocessed gray matter segments extracted
from T1-weighted images, the trained algorithm generates an estimatedbrain age. The concept of the brain age estimation is illustrated in Fig. 1.
The difference between estimated age and true chronological age yields
the so-called brain age gap estimate (BrainAGE). The absolute BrainAGE
index is large if estimated brain age and chronological age are far apart;
it is small if both values are close together. The BrainAGE index isnegative if a brain is estimated younger than its chronological age(decelerated brain aging); it is positive if a brain is estimated older
than its chronological age (accelerated brain aging). For example, if
the algorithm computes a BrainAGE index of + 5 for the brain of a
50-year-old, this individual shows the typical aging pattern of a55-year-old. Conversely, if the algorithm computes a BrainAGE indexof − 5 for the brain of a 50-year-old, this individual shows the typicalaging pattern of a 45-year-old. The BrainAGE methodology has beenvalidated across datasets, age ranges and scanner types and was foundto be robust and reliable (Franke et al., 2010).
2.5. Main Analyses

2.3. Data Preprocessing

First, we computed the BrainAGE index (i.e., the difference between
estimated brain age and chronological age) for all 100 subjects, as described above. Then, the association between BrainAGE, chronologicalage and meditation was investigated using multiple regression analysis,
where chronological age was centered on 50 years. BrainAGE wasentered as dependent variable and chronological age and group(meditators/controls) as predictors, while controlling for sex and handedness using a factorial design testing all two-way interactions.
The model was then reduced to only include signiﬁcant/inﬂuential
components. The ﬁnal model included the following predictors: age,group, sex, handedness, age × group, age × sex, age × handedness,

All T1-weighted images were processed in Matlab (http://www.
mathworks.com/products/matlab/) utilizing SPM8 (http://www.ﬁl.ion.
ucl.ac.uk/spm) and the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm.html), as recently described (Gaser et al., 2013). Brieﬂy, images
were bias corrected, spatially normalized, and classiﬁed into graymatter, white matter, and cerebrospinal ﬂuid, all within the same generative model (Ashburner and Friston, 2005). The segmentation procedure was extended by accounting for partial volume effects (Tohka
et al., 2004), applying adaptive maximum a posteriori estimations(Rajapakse et al., 1997), and using a hidden Markov Random Field
model (Cuadra et al., 2005). The resulting gray matter partitions werethen smoothed using an 8 mm full-width-at-half-maximum (FWHM)
Gaussian kernel. Subsequently, image resolution was set to 8 mm, andfurther data reduction was performed via principal component analysis
utilizing the “Matlab Toolbox for Dimensionality Reduction” (http://lvdmaaten.github.io/drtoolbox/), using the maximal number of principal components that equals to the size of the training sample, minus
1. Finally, using the gray matter data, the individual brain ages werepredicted utilizing “Spider for Matlab” (http://www.kyb.mpg.de/bs/
people/spider/main.html). For this purpose, we leveraged a recently
developed BrainAGE estimation framework, as detailed below.

Fig. 1. The brain age estimation concept. First, the algorithm is trained using preprocessedgray matter (GM) segments as well as the individual chronological ages (e.g., 19, 50,
and 86 years) from a large training sample. Then, feeding the trained algorithm withthe preprocessed GM segments (input data) from a new test sample will provide theestimated brain ages for each subject (e.g., 31 years) as the desired output data.

All subjects were scanned at the same site, using the same scanner,and following the same scanning protocol. Speciﬁcally, magnetic resonance images were acquired on a 1.5 Tesla Siemens Sonata scanner(Erlangen, Germany) using an 8-channel head coil and a T1-weighted
magnetization-prepared rapid acquisition gradient echo sequencewith the following parameters: 1900 ms repetition time, 4.38 ms echotime, 15° ﬂip angle, 160 contiguous sagittal slices, 256 × 256 mm2
ﬁeld-of-view, and 1 × 1 × 1 mm3 voxel size.

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and group × handedness. For all analyses, alpha was set at 0.05 (twotailed). Preceding this main analysis, we computed the Pearson correlation coefﬁcients between estimated brain age and chronological age
within meditators and within controls providing a validation check forthe individually obtained brain age estimates: The estimated age wassigniﬁcantly correlated with chronological age, both within controls(r = 0.883; p b 0.001) and within meditators (r = 0.866; p b 0.001).
Fig. 2 (top panel) shows the respective scatterplots illustrating the distribution of the brain age estimates within controls and meditators.
2.6. Exploratory Analyses
For exploratory purposes, we also tested for signiﬁcant correlations
between BrainAGE and the number of meditation practice years. Since
the current study used a sample with a wide age range (24–77 years)
with 62% of all meditators being ≥50 years old, we repeated the aforementioned correlation analysis while statistically removing the varianceassociated with chronological age. In addition, we performed anotherset of analyses comparing BrainAGE between less experienced andmore experienced meditators using a median split (at 16 years of practice), while keeping both groups similar in age on average. Since only avery small subsample of the older practitioners ≥50 years (n = 31) had

less than 16 years of practice (n = 5), we also repeated this analysis applying the split at 20 years, which resulted in a slightly larger subsample(n = 10). However, none of these four exploratory analyses resulted inany signiﬁcant effects.

3. Results
Our analyses revealed a main effect of Group (Beta = −7.534, p =0.047) indicating that, at age 50, brains of meditators were estimatedon average to be 7.5 years younger than brains of controls. In addition,
there was a main effect of Sex (Beta = 3.398, p = 0.009) indicatingthat female brains were estimated on average to be 3.4 years younger
than male brains. Furthermore, while the main effect of Age was not
signiﬁcant (Beta = 0.404, n.s.), we observed a signiﬁcant age-bygroup interaction (Beta = − 0.205, p = 0.050). Follow-up stratiﬁed
analyses by Group revealed that BrainAGE did not vary as a functionof chronological age in controls (Beta = 0.033, n.s.). In contrast, a significant association between BrainAGE and chronological age was detected
in meditators (Beta = −0.144, p = 0.045) indicating that for every one
year increase in chronological age over 50 years, there was a decrease of
1 month and 22 days in the estimated brain age. Fig. 2 (bottom panel)Fig. 2. Group-speciﬁc links between age-related measures. Scatterplots and regression lines were generated separately for controls (circles) and meditators (triangles). Top panel: The
X-axes display the chronological age; the Y-axes display the estimated brain age (the dashed lines show the conﬁdence interval). Bottom Panel: The X-axes display the chronologicalage; the Y-axes display the BrainAGE index (negative values indicate that participants' brains were estimated as younger than their chronological age, positive values indicate thatparticipants' brains were estimated as older).

E. Luders et al. / NeuroImage 134 (2016) 508–513

shows the respective scatterplots illustrating the distribution ofBrainAGE within controls and meditators.
4. Discussion
We applied a recently developed and validated high-dimensional
pattern recognition approach estimating the brain ages of 50 longterm meditators and 50 controls based on their structural brain images.
Although both groups were perfectly matched for age, our analyses revealed signiﬁcantly lower brain ages in meditators (i.e., more thanseven years younger at age ﬁfty) than in controls. These ﬁndings seemto imply a decreased brain aging in meditation practitioners.

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an additional 1 month and 22 days younger than their chronologicalage.
As reviewed elsewhere (Fox et al., 2014), a few studies have investigated the longitudinal effects of meditation on brain structure (albeit
only over short follow-ups) revealing actual meditation-induced
increases of brain tissue. Our ﬁndings are consistent with those reports
in that if meditation practice is associated with increased brain volumes,
it may lead to seemingly younger brains. In other words, one of thereasons that the applied high-resolution pattern-based algorithm revealed lower brain ages in meditators may be that tissue concentrationsin speciﬁc brain regions (i.e., those deemed as age-relevant when training the algorithm) were higher than what chronological age would
dictate.

4.1. Correspondence With Previous Findings
4.2. Possible Underlying Mechanisms
The assumption of younger brains and/or slower brain aging in
meditation practitioners is in close agreement with previous ﬁndings
by other investigators as well as by our own group. For example, a
study by Lazar et al. (2005) focusing on cortical thickness in two
predetermined brain areas (right insula and right frontal cortex) exposed an age-by-group interaction for the right frontal region, with signiﬁcant age-related decreases in the control group, but non-signiﬁcantdecreases in the meditation group. Moreover, it was reported that “theaverage cortical thickness of the 40–50-year-old meditation participants was similar to the average thickness of the 20–30-year-old meditators and controls” (Lazar et al., 2005).
Another study by Pagnoni and Cekic (2007) focused on whole-brain
as well as voxel-wise gray matter. With respect to whole-brain graymatter, there was a trend for a signiﬁcant age-by-group interaction“with an estimated rate of change of − 4.7 ml/year for the control
group versus + 1.8 ml/year for the meditators group” (Pagnoni andCekic, 2007). Moreover, the study exposed marginally signiﬁcant agerelated decreases in the control group contrasting increases in the meditation group (although the latter was non-signiﬁcant). With respect tovoxel-wise gray matter, the same study revealed a signiﬁcant age-bygroup interaction within the left putamen; follow-up analyses stratiﬁedby group conﬁrmed the observed whole-brain pattern also within the
putamen, with signiﬁcant negative correlations in controls and a trend
for signiﬁcant positive correlations in meditators (Pagnoni and Cekic,2007).
These two earlier studies pioneering this ﬁeld of research preceded
our own, more recent aging-related analyses focusing on fractional anisotropy in twenty ﬁber tracts (Luders et al., 2011), whole-brain andvoxel-wise gray matter throughout the entire brain (Luders et al.,2014), as well as subiculum-speciﬁc gray matter volumes (Kurth et al.,2015a). These three latter studies were based on identical or overlapping samples with the current study. Therefore, caution is warranted
to not overly interpret these outcomes in relation to our present ﬁndings. Nevertheless, given that each of the studies focused on an entirely
different brain measure, it seems at least worth mentioning that study
outcomes unanimously support the notion that meditation is associatedwith reduced age-related brain atrophy.
All of these aforementioned studies (Lazar et al., 2005; Pagnoni andCekic, 2007; Luders et al., 2011, 2014; Kurth et al., 2015a) were based onstandard correlation analyses using chronological age and various brain
attributes (e.g., voxel-wise gray matter, point-wise cortical thickness, or
tract-speciﬁc fractional anisotropy) as variables of interest. In contrast,
the current study utilized an established machine learning framework
capturing the complex and multidimensional aging pattern across theentire brain and effectively translating it into one single score. Using
this highly informative index as dependent variable, we did not only
compare the estimated brain ages between groups (meditators vs.
controls), but also examined whether associations between chronological age and estimated brain age vary over time. Indeed, our analyses
revealed an intriguing effect conﬁned to the group of meditators:
for every year after the age of ﬁfty, meditators' brains seem to become

An unresolved question relates to the nature of mechanisms underlying a possible age-defying effect associated with mindfulness practice.
As discussed in detail elsewhere (Luders, 2014; Luders et al., 2014;
Kurth et al., 2015a), different mechanisms seem plausible, either alone
or in conjunction with each other. For example, it is possible that engaging in intense mental processes (e.g., as occurring during meditation)induces dendritogenesis, synaptogenesis, myelinogenesis and/or evenneurogenesis in the adult brain (Eriksson et al., 1998). Similar eventson the micro-anatomical level might occur when, as a consequence of
meditation, there is a regular and stronger than normal activation ofvarious neurochemical systems or brain structures associated with positive mood, attention, memory, self-reﬂection, etc. In all cases, meditation could lead to relative increases of tissue in speciﬁc brain regions
over time. In parallel, it is possible that meditation slows down orcounteracts brain tissue loss by setting off biological processes withdownstream effects such as the inhibition of cellular apoptosis or
the stimulation of cellular preservation/restoration by affecting
speciﬁc pathways. For example, as recently discussed (Innes and Selfe,2014), meditation might reduce activation and reactivity of thesympathoadrenal system and the HPA axis, shift the autonomic nervous
system from primarily sympathetic to parasympathetic, and stimulate
telomerase activity while decreasing pro-inﬂammatory processes andthe production of reactive oxygen species, altogether positively affecting resistance to neurodegeneration. If mechanisms underlying tissue
gain and tissue conservation work effectively together, the resulting
net effect might be sufﬁciently strong, so that there will be an increaseor less decrease in brain tissue globally and/or locally over time. Thismight also partly explain the aforementioned observation by Pagnoni
and Cekic (2007) that the putamen's gray matter showed a trend for apositive correlation with age in meditators (while showing a signiﬁcantnegative correlation in controls).
To summarize, normal aging is associated with a signiﬁcant loss ofbrain tissue. Thus, meditation-induced tissue gain and/or meditationinduced tissue conservation might ultimately manifest as a reduced
brain age (i.e., cerebral measures characteristic of younger brains) as
we have reported when comparing the mean brain age index between
meditators and controls at age ﬁfty. If meditation-related beneﬁts forthe human brain accumulate over time, additive age-defying effects
might become evident in later years of life, as also currently observedin our sample of long-term meditation practitioners after the age ofﬁfty (i.e., the higher the chronological age, the larger the discrepancy between chronological age and estimated brain age towards relatively
younger brains in older practitioners).
4.3. An Intriguing Sex EffectLast but not least, while it was not the main focus of our study but
merely a by-product of including sex as a covariate and testing for
all two-way interactions, we observed striking differences between
men and women with respect to their estimated brain ages. More

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speciﬁcally, female brains at age 50 were estimated more than three
years younger than male brains. This sex effect was evident in the combined sample (i.e., independent of meditation status) and seems to be inagreement with previous epidemiological data indicating that women
not only tend to live longer but also show somewhat less age-related
brain atrophy (Coffey et al., 1998; Xu et al., 2000; Raz et al., 2004).
4.4. Strengths, Limitations and Future Studies
This study had a number of limitations but also several strengths.
Due to its cross-sectional nature, causality of the reported effects cannot
be resolved. It is possible, for example, that brains of those who engage
in meditative practices are inherently different from those who do not.
The level of education might be another confound (Steffener and Stern,
2012; Mortby et al., 2014) and could have driven, at least partly, the outcomes of the current study: While meditators and controls were somewhat comparable in that 90% of the meditators and 96% of the controls
had at least some college experience, the proportion of individuals withMaster's degrees was higher among meditators than controls (46% versus 20%). At the same time though, the proportion of individuals with
only high school degrees was lower among controls than meditators(4% versus 10%). It is also possible that certain lifestyle factors associatedwith meditation underlie our ﬁndings. For example, if meditators differ
from non-meditators with respect to their diet, physical exercise, alcohol consumption, smoking habits, or general health status – all ofwhich have been reported to signiﬁcantly affect aging processes in thehuman brain (Iso et al., 2004; Erickson et al., 2011; Duriez et al., 2014;
Cherbuin et al., 2015; Jacka et al., 2015) – some of the currently reported
effect (i.e., younger brains in meditation practitioners) may potentiallybe due to group differences in those variables.
Future studies will enhance this ﬁeld of research by capturing such discriminating features between meditators and controls (aside from the actual practice of meditation) and either statistically remove the impact ofpotential moderator variables or use them as inclusion/exclusion ormatching criteria during subject selection. Moreover, longer term longitudinal follow-ups will be necessary to resolve the nature/nurture debate
and to elucidate the role meditation may play in brain aging overall. Particular strengths of this study are the methodologically solid approach, thelarge sample size, the wide age range, and the fact that meditators andcontrols were closely matched on sex and age and also came from thesame population. In addition to characterizing samples with respect toconfounding variables and adopting longitudinal designs, future studies
should further scrutinize the underlying biological mechanisms which underpin a beneﬁcial effect of meditation, speciﬁcally with regard to HPA
axis modulation, neurogenesis, apoptosis, inﬂammation and oxidative
stress.
Disclosure Statement
There are no actual or potential conﬂicts of interest.
Acknowledgments
We wish to thank all meditators for their participation in our study
and we are grateful to Trent Thixton who assisted with the acquisitionof the image data. NC is funded by the Australian Research Council future fellowship number 120100227.
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