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Journal of the International Neuropsychological Society (2016), 22,1–12.
Copyright © INS. Published by Cambridge University Press, 2016.
doi:10.1017/S1355617716000850
Relationship of Lutein and Zeaxanthin Levels to Neurocognitive
Functioning: An fMRI Study of Older Adults
Cutter A. Lindbergh,
1
Catherine M. Mewborn,
1
Billy R. Hammond,
1
Lisa M. Renzi-Hammond,
1
Joanne M. Curran-Celentano,
2
AND L. Stephen Miller
1,3
1
Department of Psychology, University of Georgia, Athens, Georgia
2
Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Kendall Hall, Durham, New Hampshire
3
Bio-Imaging Research Center, Paul D. Coverdell Center, University of Georgia, Athens, Georgia
(RECEIVED April 9, 2016; FINAL REVISION September 7, 2016; ACCEPTED September 12, 2016)
Abstract
Objectives: It is well known that the carotenoids lutein (L) and zeaxanthin (Z) improve eye health and an accumulating
evidence base suggests cognitive benefits as well. The present study investigated underlying neural mechanisms using
functional magnetic resonance imaging (fMRI). It was hypothesized that lower L and Z concentrations would be
associated with neurobiological inefficiency (i.e., increased activation) during cognitive performance. Methods:
Forty-three community-dwelling older adults (mean age =72 years; 58% female; 100% Caucasian) were asked to learn
and recall pairs of unrelated words in an fMRI-adapted paradigm. L and Z levels were measured in retina (macular
pigment optical density) and serum using validated procedures. Results: Following first-level contrasts of encoding and
retrieval trials minus control trials (p<.05, family-wise error corrected, minimum voxel cluster =8), L and Z were found
to significantly and negatively relate to blood-oxygen-level-dependent signal in central and parietal operculum cortex,
inferior frontal gyrus, supramarginal gyrus, planum polare, frontal and middle temporal gyrus, superior parietal lobule,
postcentral gyrus, precentral gyrus, occipital cortex bilaterally, and cerebellar regions. Conclusions: To the authors’
knowledge, the present study represents the first attempt to investigate neural mechanisms underlying the relation of
L and Z to cognition using fMRI. The observed results suggest that L and Z promote cognitive functioning in old age by
enhancing neural efficiency. (JINS, 2016, 22,1–12)
Keywords: Aging, Cognition, Diet, Food, Magnetic resonance imaging, Carotenoids
INTRODUCTION
The xanthophylls lutein (L) and zeaxanthin (Z) are among
600 naturally occurring carotenoids that must be acquired
via diet, predominantly through consumption of green leafy
vegetables and colored fruits. Of the 30–40 carotenoids
present in human sera, generally speaking, only L and Z cross
the blood–retina barrier to form macular pigment (Bone,
Landrum, & Tarsis, 1985). L and Z also preferentially
accumulate in brain tissue, including frontal, occipital, and
temporal cortices, as well as the cerebellum and pons,
accounting for 66–77% of total brain carotenoid levels (Craft,
Haitema, Garnett, Fitch, & Dorey, 2004; Johnson et al., 2013;
Vishwanathan, Kuchan, Sen, & Johnson, 2014). As isomers
with identical chemical compositions and extremely similar
structures (Krinsky, 2002), L and Z are often administered
together in clinical trials (e.g., Chew et al., 2014) and
their effects are routinely considered conjointly in analyses
(e.g., Vishwanathan, Iannaccone, et al., 2014).
L and Z have demonstrated potential to benefit a range
of neurodegenerative disorders, such as age-related macular
degeneration, diabetic retinopathy, dementia, and Hunting-
ton’s disease (Arnal, Miranda, Barcia, Bosch-Morell, &
Romero, 2010; Binawade & Jagtap, 2013; Chew et al.,
2014; Feart et al., 2016; Scanlon et al., 2015; Wang, Shinto,
Connor, & Quinn, 2008). Results also appear to support a role
of the macular carotenoids, not only in reducing the probability
of age-related disease, but also in preventing many of the
changes that tend to precede those diseases. For instance,
preliminary data suggest a connection between L and Z levels,
measured in serum and in retina, and executive cognitive
functions, verbal fluency, attention, logical reasoning,
Correspondence and reprint requests to: L. Stephen Miller, Department
of Psychology, Psychology Building, University of Georgia, Athens,
Georgia 30602. E-mail: lsmiller@uga.edu
1
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psychomotor speed, perceptual speed, learning, memory,
language, visual-spatial and constructional abilities, and global
cognition (Akbaraly, Faure, Gourlet, Favier, & Beer, 2007;
Feeney et al., 2013; Renzi, Dengler, Puente, Miller, &
Hammond, 2014; Vishwanathan, Iannaccone, et al., 2014).
Dietary intake of green leafy vegetables high in L and Z
buffers older adults from global cognitive decline, as
demonstrated in longitudinal designs (Kang, Ascherio, &
Grodstein, 2005; Morris, Evans, Tangney, Bienias, & Wil-
son, 2006). Although most of the relevant data is cross-sec-
tional, in an exploratory intervention trial in older women,
docosahexaenoic acid and L supplementation were asso-
ciated with improvements in verbal fluency, memory, and
learning slope when compared to placebo (Johnson et al.,
2008; although, see Chew et al., 2015). Taken together, there
is promising evidence suggesting that L and Z exert a positive
effect on a range of cognitive outcomes in older adults.
L and Z have traditionally been measured via two primary
methods: serum and retinal levels. Serum levels of L and Z,
which are frequently combined for analyses due to their
chemical and structural correspondence (e.g., Bone,
Landrum, Dixon, Chen, & Llerena, 2000; Olmedilla-Alonso,
Beltran-de-Miguel, Estevez-Santiago, & Cuadrado-Vives,
2014; Vishwanathan, Iannaccone, et al., 2014), are driven by
recent dietary intakes and do not necessarily reflect
long-term dietary behavior, unless dietary behavior is stable
in the individuals measured (Beatty, Nolan, Kavanagh, &
O’Donovan, 2004). Some studies have found that serum L
and Z levels remain generally constant with advancing age
(e.g., Cardinault et al., 2003) while others have observed
modest increases (e.g., Olmedilla-Alonso et al., 2014) likely
related to corresponding increases in dietary intake (Johnson,
Maras, Rasmussen, & Tucker, 2010). However, serum L and
Z is lower in individuals with mild cognitive impairment
and Alzheimer’s disease compared to cognitively healthy
individuals (Nolan et al., 2014; Rinaldi et al., 2003). In the
retina, L and Z preferentially accumulate in the macula as
macular pigment (MP), and optical density of the macular
pigment layer (MPOD) can be measured non-invasively
using psychophysical techniques. The most common method
of measuring MPOD, heterochromatic flicker photometry,
has been well-validated (Hammond, Wooten, & Smollon,
2005) and may serve as a biomarker of L and Z concentra-
tions in brain tissue (e.g., Vishwanathan, Iannaccone, et al.,
2014; Vishwanathan, Neuringer, Snodderly, Schalch, &
Johnson, 2013). Like serum levels, MPOD appears to be
lower in individuals with cognitive impairment and dementia
(Feeney et al., 2013; Nolan et al., 2014; Renzi et al., 2014;
Vishwanathan, Iannaccone, et al., 2014). Unlike serum
levels, one advantage of MPOD is that it represents a measure
of L and Z already incorporated into central nervous system
tissue and thus may be a better indicator of longer-term
dietary intakes.
Although serum levels of L and Z are positively correlated
with MPOD (typical rvalues are around 0.30; e.g., Renzi,
Hammond, Dengler, & Roberts, 2012), they represent distinct
measures (e.g., Beatty, et al., 2004; Burke, Curran-Celentano, &
Wenzel, 2005). For example, one study assessed both serum
L and Z and MPOD each month for 24 months and found that
MPOD mean and variance were relatively stable, while serum
concentrations were more variable; additionally, variations in
MPOD and serum concentrations were not linearly related
(Nolan et al., 2006). Another study demonstrated that after
discontinuing supplementation of L and Z, serum concentrations
quickly returned to baseline, whereas changes in MPOD
lasted for up to 100 days (Beatty et al., 2004; Hammond et al.,
1997). These findings are consistent with the interpretation that
serum concentrations of L and Z more closely reflect recent or
“acute”dietary factors, whereas MPOD, which has a slower
biological turnover, reflects more stable L and Z levels acquired
over time.
The mechanisms underlying the relation of L and Z to
cognitive functioning remain to be fully elucidated. In light of
findings suggesting oxidative stress and inflammation play
important roles in dementia and cognitive decline more
broadly (Engelhart et al., 2004; Finkel & Holbrook, 2000;
Pappolla et al., 2002; Teunissen et al., 2003), it is possible
that L and Z exert neuroprotective effects via their anti-
oxidant and/or anti-inflammatory properties (Johnson, 2012).
More specifically, L and Z quench reactive oxygen molecules
to prevent free radical attack (Johnson, 2014) while altering
inflammation-related gene expression (Bian et al., 2012),
pro-inflammatory factor production (Li et al., 2012), and
inflammatory cytokine signaling to buffer neural damage
(Sasaki et al., 2009). Carotenoids may also impact cognition
by enhancing interneuronal communication through facilita-
tion of signaling compound exchange at gap junctions
(i.e., cell-to-cell channels) and by providing structural sup-
port to synaptic membranes (Krinsky, Mayne, & Sies, 2004;
Stahl & Sies, 2001). Studies using animal models have
supported the possibility of such mechanisms, demonstrating
that carotenoid administration reduces neurodegeneration,
ameliorates oxidative stress and inflammation, and improves
cholinergic and mitochondrial dysfunction in brain regions
relevant to cognitive aging, such as cerebral cortex and
hippocampi (Arnal et al., 2010; Binawade & Jagtap, 2013;
Kuhad, Sethi, & Chopra, 2008; Muriach et al., 2006;
Nakashima et al., 2009). Importantly, these neuroprotective
actions are associated with improvements in cognitive
performance on tests of learning and memory (Binawade &
Jagtap, 2013; Kuhad et al., 2008; Nakashima et al., 2009).
The present study aimed to further illuminate mechanisms
by which L and Z relate to cognitive functioning in older
adults using functional magnetic resonance imaging (fMRI)
of verbal memory performance. fMRI has demonstrated
sensitivity to changes in neural activation in healthy and
pathologically aging older adults on memory tasks as well as
to the effects of memory-enhancing interventions, including
both drug and antioxidant therapies (e.g., Bookheimer et al.,
2013; Dickerson et al., 2004; Gutchess et al., 2005; Lorenzi
et al., 2011; Saykin et al., 1999). To the authors’knowledge,
this represents the first investigation in which a neuroimaging
technique was used to provide an in vivo assessment of the
impact of L and Z on brain function.
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According to the scaffolding theory of aging and cognition
(STAC), cognitive functioning is preserved in the face of
age-related neural insult through an ongoing process of
reorganization of existing neural connections and recruitment
of additional neural circuitry (Park & Reuter-Lorenz, 2009).
“Compensatory scaffolding”is considered to be an adaptive,
dynamic process that occurs in response to intrinsic (e.g.,
biological aging) or extrinsic (e.g., task demands) neural
challenges to maintain cognitive performance (Park &
Reuter-Lorenz, 2009). It is reflected in neuroimaging studies
as increased bilateral activation and/or overactivation in
certain regions (e.g., prefrontal cortex; e.g., Cabeza et al.,
1997; Cabeza, Anderson, Locantore, & McIntosh, 2002;
Fera et al., 2005; Haung, Polk, Goh, & Park, 2012). The
notion that additional neural activity reflects a compensatory
neurocognitive process is supported by reduced hemispheric
asymmetry as the complexity of a given task increases (e.g.,
Banich, 1998; Hillary, Genova, Chiaravalloti, Rypma, &
DeLuca, 2006), while practice on a given task refines neural
networks such that they become less dispersed and more
specific (e.g., Petersen, van Mier, Fiez, & Raichle, 1998).
With respect to verbal memory, neuroimaging findings
have suggested that age-related cognitive changes, genetic
vulnerability to disease, and early stages of neurodegenera-
tive conditions such as dementia are associated with more
pronounced and bilateral patterns of brain activity, sugges-
tive of increased neurocognitive effort relative to healthy
counterparts (e.g., Bookheimer et al., 2000; Cabeza et al.,
1997; Dickerson & Sperling, 2008).
In the present study, we evaluated whether L and Z levels
are cross-sectionally related to neurobiological efficiency in
community-dwelling older adults via promotion of more
honed neural networks and reduced need for compensatory
scaffolding in response to age-related neural deterioration.
MPOD and serum concentrations of L and Z were assessed
alongside performance on a verbal memory task in which
participants were asked to learn and recall word pairs
(Bookheimer et al., 2000). Lower MPOD and serum L and Z
were hypothesized to predict greater compensatory recruit-
ment during memory encoding and retrieval as evidenced by
increased neural activity required to meet task demands (i.e.,
neurobiological inefficiency). For memory encoding, this
pattern of activity was anticipated in medial temporal lobe,
supramarginal and angular gyri, precuneus, dorsolateral
and ventrolateral prefrontal cortex, anterior and posterior
cingulate gyrus, Broca’s area, cerebellum, and premotor
areas, consistent with regions-of-interest that have demon-
strated involvement in verbal memory in other studies (e.g.,
Binder, Desai, Graves, & Conant, 2009; Bookheimer et al.,
2000; Cabeza & Nyberg, 2000; Clément & Belleville, 2009).
With the exception of the cerebellum, brain activation was
generally expected to be left-lateralized (Binder et al., 2009;
Cabeza & Nyberg, 2000). A similar frontotemporoparietal
network was hypothesized for memory retrieval, although
brain activity was expected to show greater tendency for
right-lateralization and to evidence greater involvement of
anterior prefrontal cortex and medial parieto-occipital areas,
including retrosplenial cortex and cuneus (Cabeza & Nyberg
2000). Behaviorally, greater MPOD and serum L and Z
were expected to predict enhanced cognitive performance
on measures of word recall. Given that serum and retinal
concentrations of L and Z and their isomers have been
positively related in previous studies, we expected that both
measures would follow the same general pattern of negative
relation to brain activity and positive relation to behavioral
measures of word recall. However, because serum con-
centrations represent acute L and Z levels and MPOD
represents acquired levels, results were not expected to be
identical in all hypothesized regions-of-interest.
METHOD
Participants
Data for the present study were derived from a larger inter-
vention study evaluating the relationship between cognition
and diet in community-dwelling older adults (65–86 years)
recruited from the surrounding area via advertisements,
flyers, and electronic media (e.g., listservs). Exclusion
criteria included left-handedness, traumatic brain injury, age-
related macular degeneration in either eye, gastric conditions
with potential to interfere with L/Z absorption (gastric ulcer,
gastric band or bypass, Crohn’s disease, ulcerative colitis),
corrected visual acuity poorer than 20:40, MRI incompat-
ibility, and/or evidence of dementia or other neurological
disorder. Of the participants who were eligible for inclusion
and for which neuroimaging, serum, and MPOD data were
collected (N=50), 6 were unable to complete the MRI
process (e.g., physical discomfort, fatigue, or obvious failure
to follow task instructions) and behavioral data from one
individual were lost due to technical malfunction, leaving
a sample size of 43 for final analyses. Participants were
compensated $100 for their time and effort. The study was
approved by the University of Georgia Institutional Review
Board for safety and ethical treatment of participants, and the
tenets of the Declaration of Helsinki were adhered to at all
times by all study personnel.
Measures
Wechsler test of adult reading
The Wechsler Test of Adult Reading (WTAR; The
Psychological Corporation, 2001) was administered to
estimate premorbid intellectual functioning (Wechsler,
2001). The WTAR provides a Full-Scale Intelligence
Quotient (FSIQ) estimate based on an algorithm that
incorporates an examinee’s ability to pronounce a list of
words as well as demographic (i.e., age, education, race, sex,
and geographic region) variables.
Geriatric depression scale
Depressive symptomatology was assessed using the Geriatric
Depression Scale (GDS; Yesavage et al., 1983). The GDS is a
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self-report instrument comprised of 30 items of yes/no format
and is scored on a 0–30 scale, with higher scores indicating
greater levels of depression.
Serum lutein and zeaxanthin
Details of serum analysis can be found elsewhere
(Handelman, Shen, & Krinsky, 1992; Qin et al., 2008).
Briefly, a total of 7 mL of whole blood was collected via
venipuncture by a certified phlebotomist. After collection,
samples were immediately placed on ice and centrifuged cold
for 15 min. Following centrifugation, serum was collected
and aliquoted into 1-mL cryotubes and frozen at −80°C for
analysis.
Frozen serum aliquots were thawed to a temperature of
23°C, and precipitated with ethanol. The fat soluble car-
otenoids were extracted from the aqueous suspension with
n-hexane/chloroform. After centrifugation, an aliquot of the
organic phase was evaporated to dryness, re-dissolved in
mobile phase and prepared for injection using an autoinjec-
tion system. L/Z was analyzed using a Hewlett Packard/
Agilent Technologies 1100 series high performance liquid
chromatography (HPLC) system with a photodiode array
detector (Agilent Technologies, Palo Alto, CA). A 5 μm, 200
A° polymeric C
30
reverse-phase column (Pronto-SIL,
MAC-MOD Analytical Inc., Chadds Ford, PA) was used to
separate the analytes.
The HPLC mobile phase solvent A consists of methanol/
tert-butyl methyl ether/water (83:15:2, v/v/v, with 1.5%
ammonium acetate in the water) and solvent B is methanol/
tert-butyl methyl ether/water (8:90:2, v/v/v, with 1%
ammonium acetate in the water). The gradient procedure at a
flow rate of 1 mL/min begins at 100% Solvent A for 2min to
70% Solvent A over a 6-min linear gradient and held at 70%
A for 3 min, then a 10-min linear gradient to 45% Solvent A
and a 2-min hold at 45% Solvent A, then a 10-min linear
gradient to 5% Solvent A, a 4-min hold at 5% Solvent A and,
finally, a 2-min linear gradient back to 100% Solvent A. The
system is held at 100% Solvent A for 10 min for equilibration
back to initial conditions (Qin et al., 2008).
Serum L and Z were analyzed separately via HPLC. To
obtain a combined serum L+Z value, serum L levels (μmol/L)
were added to serum Z levels (μmol/L). The combined L+Z
value was used in all subsequent analyses. To assess the daily
and long-term laboratory performance of the HPLC plasma
analytics, dedicated control plasma was used following
standardization with SRM 968 c (Standard Reference
Materials, National Institute of Standards and Technology,
Gaithersburg, MD).
Macular pigment optical density
Macular pigment optical density (MPOD) was evaluated
using customized heterochromatic flicker photometry, as
described previously (Stringham et al., 2008). Briefly, a
macular densitometer (Macular Metrics; Rehoboth, MA) was
used to present participants with a 1° visual stimulus that
consisted of two narrow-band LED-based light sources,
peaking at 460 nm and 570 nm. The light sources were
presented in square-wave, counter-phase orientation to present
the appearance of flicker. Before measurement, each
participant’s critical flicker fusion frequency was measured
using only the mid-wave portion of the stimulus, so that the
task could be customized to the individual viewer. Following
determination of customized flicker sensitivity, participants
were asked to fixate on a black dot displayed at the disk’score.
The radiance of the lower (i.e., 460 nm) waveband was
manipulated relative to the 570 nm component to assess the
point at which flickering was no longer perceivable. This
sequence was again conducted with a 2° target and fixation
point at 7° nasally to allow a reference measurement in the
parafovea (where MPOD approaches zero). The two loci
(i.e., 30 min, derived from the 1° target, and 7° of retinal
eccentricity) were then compared to provide the MPOD
measurement at 30 min of retinal eccentricity.
Neuroimaging
fMRI task
Participants completed a block design verbal learning task
involving unrelated word pairs (e.g., “UP”and “FOOT”)
conceptually based on the Wechsler Memory Scale Verbal
Paired Associates (Wechsler, 2009) and similar to previous
fMRI paradigms (e.g., Bookheimer et al., 2000, 2013;
Braskie, Small, & Bookheimer, 2009). The task was pro-
grammed using E-Prime software (version 1.2, Psychology
Software Tools, Inc., Pittsburgh, PA) and presented through
MRI compatible goggles (Resonance Technology Inc.,
Northridge, CA). Participants responded using a pair of
2-button Cedrus Lumina LU400 MRI compatible response
pads (Cedrus, San Pedro, CA). The task consisted of
10 separate learning blocks, control blocks, retrieval
blocks, and fixation blocks, presented sequentially in the
aforementioned order (i.e., learning, control, retrieval,
fixation) for every participant (see Figure 1).
In the learning blocks, the first word of each pair was
presented alone on the left side of the screen (1 s) followed by
presentation of the second word on the right side of the
screen, such that both words were viewable simultaneously
(2 s). There were a total of 10 word pairs, 5 of which were
presented in each encoding block. Participants were instruc-
ted to respond with their right index finger whenever the
second word in the pair appeared, to help verify attention
during learning. During the retrieval portion of the task,
participants were presented with the first word in each pair
(3 s) and asked to mentally recall (to avoid head motion) the
second word, consistent with procedures used in similar
fMRI-adapted verbal learning paradigms (e.g., Bookheimer
et al., 2000, 2013). Participants were instructed to respond
with their right index finger if they remembered the second
word or to respond with their left index finger if they did not
(maximum score: 50).
Learning and retrieval blocks were interspersed with a
control task analogous to the learning block except that
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Xs (i.e., “XXXXX”) and Ys (i.e., “YYYYY”) were presented
instead of word pairs. Participants responded with their right
index finger whenever the Ys appeared on the screen.
Fixation blocks consisted of a crosshair presented for 6 s.
Immediately following the scan, participants were asked to
engage in free recall (maximum score: 10) followed by cued
recall (maximum score: 10) of the “second word”in each pair
to help verify task engagement within the scanner.
MRI acquisition
A General Electric (GE; Waukesha, WI) 3 Tesla Signa HDx
MRI system was used to acquire all scans. Structural scans
were collected using a high-resolution three-dimensional
T
1
-weighted fast spoiled gradient recall echo sequence
[repetition time (TR) =7.5 ms; echo time (TE) =<5 ms;
field of view (FOV) =256 × 256 mm matrix; flip angle =
20°; slice thickness =1 mm; 154 axial slices; voxel size =
0.94 × 0.94 × 1 mm] with a total acquisition time of 6 min,
20 s. This protocol covered the top of the head to the
brainstem and collected 176 images.
Functional scans were aligned to the anterior commissure-
posterior commissure line and collected axially using a
T
2
*-weighted single shot echo planar imaging (EPI) sequence
(TR =1500 ms; TE =25 ms; 90° RF pulse; acquisition
matrix =64 × 64; FOV =220 × 220 mm; in-plane resolution,
220 × 64 mm; slice thickness =4 mm; 30 interleaved axial
slices; voxel size =3.43 × 3.43 × 4 mm) with an acquisition
time of 12min, 24 s. The EPI sequence covered the cortical
surface and a portion of the cerebellum, and consisted of
290 volumes. A pair of magnitude and phase images was
acquired, lasting 1 min, 40 s each, for fieldmap-based unwarping
(TR =700 ms; TE =5.0/7.2 ms; FOV =220 × 220 mm matrix;
flip angle =30°; slice thickness =2 mm; 60 interleaved slices;
voxel size =1.72×1.72×2mm).
Data analysis
Statistical Parametric Mapping (SPM12, Wellcome Depart-
ment of Cognitive Neurology, London, UK) was used to
process and analyze the data. Data were first converted from
GE DICOM to NIFTI format with the dcm2nii conversion tool
(Rorden, 2007). Preprocessing of functional data included slice
time correction to adjust for the non-sequential, interleaved
acquisition and realignment of functional images to the first
image of the functional scan to correct for head movement.
Fieldmaps were created to realign and unwarp images to
account for phase and magnitude variations during the scan.
Anatomical scans were co-registered to the first image of the
functional scan followed by registration of anatomical and
functional imagesto the Montreal Neurological Institute (MNI)
template. The anatomical image was segmented to differentiate
brain tissue (i.e., white matter and gray matter), cerebrospinal
fluid, bone, non-brain soft tissue, and air. Deformation fields
were created and applied to functional images to allow spatial
normalization to MNI space. Finally, images were smoothed
using a 6.75-mm FWHM Gaussian filter.
Following pre-processing, activation maps of encoding
minus control task and retrieval minus control task were
created using the General Linear Model (SPM12b). All trials
were included in analyses, regardless of occasional failure to
respond or self-perceived recall success. A statistical threshold
of p<.05, family-wise-error (FWE) corrected, and a minimum
of eight contiguous voxels were selected given optimal balance
between Type I and Type II error rates (Lazar, 2008).
Regression-based analyses were conducted to determine the
relation of L and Z levels to voxel activity within hypothesized
regions-of-interest during memory encoding and recall.
The relation of L and Z levels to behavioral measures of
recall was also evaluated using regression analyses. More
specifically, the number of self-reported successful retrievals
across the entire task and on the final 10 retrieval trials only
(the assumed point of maximal learning), as well as the
number of verified cued successful retrievals measured
immediately post-scan, were considered as dependent
variables in three separate regressions.
Procedure
Following recruitment and screening for exclusion criteria
(described above), data were collected across three testing
Fig. 1. Verbal Learning Task. The above figure provides a visual schematic of the progression of the verbal learning task. The four
blocks (i.e., learning, control, retrieval, and fixation) were presented a total of 10 times, resulting in a total acquisition time of 12 min, 24 s.
Five different words pairs were presented in each learning block and 5 sets of XXXXX –YYYYY pairings were presented in each control
block. During each retrieval block, participants were asked to recall the second word for five different word pairs.
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sessions. During the first session, written informed consent
was obtained and MPOD was measured. Participants com-
pleted the GDS and WTAR in the second session, as well
as a handful of other measures that were part of the larger
intervention study and not a focus of the present analyses.
The third session was conducted at the University of Georgia
Bio-Imaging Research Center, which houses the MRI
scanner. Before being placed in the MRI environment,
participants practiced an abbreviated version of the verbal
learning task on a desktop computer to ensure understanding
of task directions and provide the opportunity to ask
questions. The MRI protocol included structural imaging
first, followed by collection of phase and magnitude images,
and finally acquisition of functional data during the verbal
learning task. Participants were debriefed and thanked at the
end of the testing session.
RESULTS
Sample Characteristics and Verbal Learning
Behavioral Responses
Serum L+Z levels, MPOD, and demographic information
including age, years of formal education, gender and racial
composition, and estimated intellectual functioning are
presented in Table 1. It is notable that the sample was entirely
Caucasian and tended to be well educated.
Table 1 also provides descriptive statistics related to
behavioral performance on the verbal learning task. While in
the scanner, participants, on average, self-reported recall of
the second word in the pair on 37 of the 50 total recall trials
(74%). The group average self-reported (within scanner)
recall during the last two recall blocks only (the assumed point
of maximal learning) was 9 of the 10 total word pairs (90%).
As expected, self-reported recall within the scanner
significantly correlated with actual cued recall immediately
following the scan (r=0.45; p=.002), differing from each
other by less than two words, on average (mean difference =
1.67; SD =2.00, 3 participants with discrepancy >4words).
The observed correlation provides evidence that participants
were engaged in the task and actively attempting to learn and
recall word pairs within the scanner. In addition, discrepancy
scores between within-scanner recall and actual post-scan
recall were unrelated to MPOD (p=.593) or serum L+Z
(p=.073).
Age and education were considered as potential covariates
in analyses but given nonsignificant zero-order bivariate
correlations to MPOD (r=.17; p=.279, and r=.10;
p=.521, respectively), serum L+Z (r=−.02; p=.926, and
r=.07; p=.681, respectively), and behavioral measures of
verbal learning, they were not included in the regression
models.
Whole-Brain Analyses
Using a FWE corrected p<.05 and minimum of eight con-
tiguous voxels, the encoding minus control contrast revealed
widespread activation in regions commonly associated with
verbal learning task performance, including left inferior
frontal regions (Broca’s area), middle frontal gyri, hippo-
campus, precentral gyrus, and occipital lobe (see Figure 2).
The recall minus control contrast similarly revealed diffuse
activation in several regions including paracingulate gyri,
insular cortex, middle frontal gyrus, and occipital lobe
(see Figure 3).
Lutein and Zeaxanthin Levels and fMRI
Performance
Behavioral
Serum L+Z and MPOD were unrelated to overt within-
scanner behavioral performance on the verbal learning task.
More specifically, MPOD was not a significant predictor of
the number of self-reported successful retrievals across the
entire paradigm (r=−.001; p=.993) nor across the last
10 trials only (i.e., the final block; r=.103; p=.511).
Similarly, serum L+Z concentrations were not significantly
related to word retrieval across the entire paradigm (r=.236;
p=.127) nor on the final block (r=.275; p=.074). MPOD
and serum L+Z levels also displayed nonsignificant relations
to actual cued recall post-scan (r=.137; p=.380, and
r=−.084; p=.591, respectively).
Functional imaging
Following the encoding minus control contrast (p<.05,
FWE, minimal voxel cluster =8), regression-based
analyses of blood-oxygen-level-dependent BOLD) signal
Table 1. Descriptive statistics (N=43)
Variable % or M(SD)
Demographics
Age (years) 71.55 (5.84)
Sex (% female) 58.14%
Race (% Caucasian) 100%
Education (years) 16.66 (3.51)
GDS 2.79 (3.20)
Predicted FSIQ
a
115.26 (7.49)
Verbal learning
Total recall (max =50) 36.84 (9.10)
Final block (max =10) 9.00 (1.33)
Post-scan cued recall (max =10) 7.33 (2.21)
Lutein and zeaxanthin
MPOD (o.d.) 0.51 (0.18)
Serum (umol/L) 0.31 (0.17)
Note. o.d. represents the log ratio of transmitted light passing through the
macula.
GDS =Geriatric Depression Scale; FSIQ =Full-Scale Intelligence Quo-
tient from the Wechsler Test of Adult Reading; MPOD =macular pigment
optical density; o.d. =optical density.
a
Data only available for 42 participants.
6C.A. Lindbergh et al.
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demonstrated that lower MPOD levels were associated with
significantly greater brain activity (i.e., neural inefficiency) in
several regions relevant to verbal learning including left
insular cortex, right middle temporal gyrus, left supramar-
ginal gyrus, and left cerebellum (p<.01; see Table 2 and
Figure 2). With respect to the recall minus control contrast
(p<.05; FWE, minimal voxel cluster =8), lower MPOD
was associated with increased activation in left inferior
frontal gyrus, left insular cortex, left planum polare, right
middle frontal gyrus, left cerebellum, and left and right
occipital pole (p<.01; see Table 3 and Figure 3). As
indicated in Tables 2 and 3, effect sizes for MPOD during
encoding and retrieval ranged from r=.36 to r=.46.
L+Z concentrations in serum similarly demonstrated a
negative relationship with BOLD signal in several brain
regions during encoding including left parietal operculum,
occipital cortex bilaterally (superior division), left postcentral
gyrus, and left precentral gyrus (p<.01; see Table 2 and
Figure 2). During recall, less L+Z in serum was associated
with increased activation in left central opercular cortex, left
superior parietal lobule, and left lateral occipital cortex
(p<.01; see Table 3 and Figure 3). As represented in
Tables 2 and 3, effect sizes for L+Z in serum during encoding
and retrieval ranged from r=.37 to r=.49.
DISCUSSION
L and Z are two carotenoids previously shown to accumulate
in human brain tissue, improve cognitive functioning, and
reduce risk of age-related degenerative diseases (e.g.,
Akbaraly et al., 2007; Johnson, 2014; Johnson et al., 2013;
Nolan et al., 2014; Rinaldi et al., 2003; Vishwanathan,
Iannaccone, et al., 2014). To date, no studies have investi-
gated the neural mechanisms underlying these relationships.
This study sought to determine whether L + Z are related to
neural efficiency during a verbal learning and memory task.
Initial whole brain analyses revealed diffuse activation in
regions commonly associated with verbal learning and
retrieval, replicating previous findings (e.g., Bookheimer
et al., 2000; Cabeza et al., 1997; Dickerson & Sperling,
2008). As expected, lower levels of both MPOD and serum
L+Z concentrations were associated with increased activation
(i.e., neural inefficiency) in several of these regions, though
results were not identical for the two measures. Overall,
serum concentrations predicted activity in regions commonly
involved in somatosensory functions (e.g., postcentral gyrus,
parietal and central operculum, superior parietal lobule;
Eickhoff et al., 2010; Molholm et al., 2006), whereas MPOD
was associated with activity in regions more involved in
Fig. 2. Panel (a) depicts whole-brain analyses of the encoding minus control contrast (independent of lutein and zeaxanthin levels)
superimposed on a single-subject anatomical template in MNI space provided by MRIcron (http://www.mricro.com/mricron/install.html).
To conserve space, only six slices were selected for visualization based on largest extent activation and thus not all voxel activity is
represented. Panel (b) displays brain activation significantly related to lutein and zeaxanthin concentrations during encoding. Areas in
green represent increased activation associated with lower MPOD levels, while areas in red represent increased activation associated with
lower serum lutein and zeaxanthin. Only six slices were selected based on largest extent activation to showcase the relation of lutein and
zeaxanthin to brain activity and thus not every significant cluster is displayed.
Lutein and zeaxanthin on older adults 7
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language processing (e.g., inferior frontal gyrus, middle
temporal gyrus, supramarginal gyrus, planum polare; Cabeza
& Nyberg, 2000; Costafreda et al., 2006; Friederici, Meyer,
& von Cramon, 2000). This pattern suggests that acute intake
of L+Z, as represented by serum concentrations, may
boost performance by aiding in somatosensory processing.
Carotenoid consumption over longer periods of time, as
represented by MPOD, may increase performance via more
efficient higher-order language skills.
Regardless of the specific cognitive functions implicated,
pathological aging and cognitive declineinlatelifehavebeen
associated with dysfunction in many of the brain regions
that demonstrated a functional relationship to L+Z levels,
including left inferior frontal gyrus (Bookheimer et al., 2000;
Clément & Bellville, 2009), insula (Xie et al., 2012), middle
temporal gyrus (Convit et al., 2000), middle frontal gyrus
(Rajah, Languay, & Grady, 2011), and central and parietal
operculum (Trachtenberg et al., 2012). The present results
are, therefore, consistent with past studies concluding that a
diet rich in L+Z may buffer pathobiological processes in old
age by enhancing neural efficiency in structures at risk for
deterioration.
Both MPOD and serum L+Z levels was associated with
neural efficiency in primary visual areas, suggesting that
these carotenoids more generally improve visual processing
(as shown behaviorally; Renzi & Hammond, 2010) beyond
the level of the retina. This finding holds particular relevance
among older adults, who exhibit a high prevalence of visual
difficulties due to conditions such as macular degeneration,
which in turn have been shown to impact functional
properties at a neural level (e.g., Baker, Peli, Knouf, &
Kanwisher, 2005).
Contrary to expectation, (serum or MP) L and Z did not
predict behavioral performance on the verbal memory task.
In many respects, however, this observation is consistent
with the STAC in the sense that individuals with lower L and
Z levels were required to recruit additional neural resources
to maintain a similar level of cognitive performance as peers
with higher L and Z levels (Park & Reuter-Lorenz, 2009).
It is also possible that there was a ceiling effect given the
generally high level of cognitive functioning apparent within
the present sample and that a more sensitive cognitive
measure with greater variability in scores would reveal a
relationship. Despite significant correlations with actual
cued recall post-scan, the reliance on self-reported cognitive
performance within the scanner limited conclusions regard-
ing task accuracy and may have influenced the observed
results.
Fig. 3. Panel (a) depicts whole-brain analyses of the recall minus control contrast (independent of lutein and zeaxanthin levels)
superimposed on a single-subject anatomical template in MNI space provided by MRIcron (http://www.mricro.com/mricron/install.html).
To conserve space, only six slices were selected for visualization based on largest extent activation and thus not all significant voxel
activity is represented. Panel (b) displays brain activation significantly related to lutein and zeaxanthin concentrations during retrieval.
Areas in green represent increased activation associated with lower MPOD levels, while areas in red represent increased activation
associated with lower serum lutein and zeaxanthin. Only six slices were selected based on largest extent activation to showcase the relation
of lutein and zeaxanthin to brain activity and thus not every significant cluster is displayed.
8C.A. Lindbergh et al.
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Another important limitation of the present study is its
cross-sectional design, which prevents conclusions regarding
the directionality of the observed relationship between L+Z
and neural efficiency. Although longitudinal studies have
indicated that carotenoid consumption preserves neurocog-
nitive health (Min & Min, 2014), other dietary factors
(e.g., antioxidant-rich fruits) left uncontrolled in the present
analyses have been shown to impact brain function as
measured with fMRI (Bookheimer et al., 2013) and the
results should be interpreted with this in mind. For example,
it is possible that L and Z levels serve as proxies for other
features of a healthful diet that relate to brain health, such
as omega-3 fatty acid intake (Witte et al., 2014), and
randomized controlled trials will be required to better
address issues of causality while isolating the effects of the
xanthophylls on neural activity.
Additionally, our sample was entirely Caucasian, highly
educated, and, generally, very cognitively healthy. Thus, the
generalizability of our findings to individuals characterized
by greater diversity in socioeconomic status, racial back-
ground, and cognitive function may be limited and represents
a critical avenue for future research. Finally, despite the
strong chemical and structural similarities between L and Z,
future neuroimaging studies may benefit from considering
their unique effects in analyses rather than combining them as
we have in the present investigation.
To our knowledge, this is the first study to investigate the
association of L and Z to cognition using fMRI. Results
indicate that L and Z concentrations, measured both acutely
(serum) and acquired (retinal), enhance neural efficiency
during verbal learning and memory in older adults. Our
findings also offer a possible neural mechanism underlying
previous findings showing a positive relation between
these carotenoids and performance on cognitive tasks (e.g.,
Feeney et al., 2013; Johnson, 2014; Renzi et al., 2014;
Vishwanathan, Iannaccone, et al., 2014). More broadly, the
present study adds to the paucity of research investigating the
critical relationship between diet and brain health, while
identifying a modifiable lifestyle factor that may serve to
promote neurocognitive functioning in the rapidly expanding
older adult population.
ACKNOWLEDGMENTS
This research project was funded in part by Abbott Nutritional
Products (Columbus, OH; research grant to B.R.H., L.M.R.,
L.S.M.) and the University of Georgia’s Bio-Imaging Research
Center (administrative support, L.S.M.). DSM Nutritional Products
(Switzerland) provided the supplements and placebos for the larger
intervention study from which the data for the present study were
derived. Additionally, L.M.R. was an employee of Abbott Nutrition
during a portion of the grant period while holding a joint
appointment at the University of Georgia. B.R.H. has consulted for
Abbott Nutrition. No other potential conflicts of interest exist for
any of the study authors, including C.A.L., C.M.M., and J.M.C.
All statistical analyses were completed independently of supporting
agencies.
Table 2. Relationship of lutein and zeaxanthin to brain activation
during encoding (N=43)
Region xyzExtent
Z-
Score
Effect
Size (r)
MPOD
L insular cortex −40 10 −14 99 3.03 0.45
L insular cortex −42 0 −10 * 2.94 0.44
R middle temporal
gyrus
62 −58 2 10 2.75 0.41
L cerebellum −10 −76 −22 11 2.52 0.38
L supramarginal
gyrus
−64 −34 26 3 2.44 0.37
Serum
L lateral occipital
cortex
−24 −74 38 45 2.96 0.44
L postcentral gyrus −20 −44 66 31 2.90 0.43
L parietal operculum
cortex
−48 −30 24 39 2.90 0.43
L precentral gyrus −58 0 32 5 2.76 0.41
R lateral occipital
cortex
36 −68 50 17 2.60 0.39
R lateral occipital
cortex
26 −78 28 7 2.48 0.37
Note. The above table includes brain activity that was significantly and
negatively associated with lutein and zeaxanthin levels during encoding
of word pairs.
MPOD =macular pigment optical density. x,y, and zcoordinates are in MNI
space (mm). L =left and R =right.
*=cluster overlap with preceding row.
Table 3. Relationship of lutein and zeaxanthin to brain activation
during recall (N=43)
Region xyzExtent
Z-
Score
Effect
size (r)
MPOD
L inferior frontal
gyrus
−42 8 24 48 3.10 0.46
L cerebellum −10 −74 −22 24 2.96 0.44
L occipital pole −12 −102 −2 9 2.78 0.41
L planum polare −46 −4−6 8 2.56 0.38
L insular cortex −38 −4−12 15 2.53 0.38
R middle frontal
gyrus
46 34 18 7 2.47 0.37
R occipital pole 16 −96 12 2 2.40 0.36
Serum
L central opercular
cortex
−48 −4 10 21 3.36 0.49
R lateral occipital
cortex
22 −68 58 9 2.56 0.38
L central opercular
cortex
−58 2 2 7 2.48 0.37
L superior parietal
lobule
−38 −42 60 4 2.45 0.37
Note. The above table includes brain activity that was significantly and
negatively associated with lutein and zeaxanthin levels during retrieval
of word pairs. x,y, and zcoordinates are in MNI space (mm).
MPOD =macular pigment optical density. L =left and R =right.
Lutein and zeaxanthin on older adults 9
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