ORIGINAL ARTICLE

Effects of PER3 clock gene polymorphisms on aging-relatedchanges of the cerebral cortex

Delphine Dewandre1 • Mercedes Atienza1 • Mayely P. Sanchez-Espinosa1 •

Jose L. Cantero1

Received: 17 December 2016 / Accepted: 7 September 2017! Springer-Verlag GmbH Germany 2017

Abstract Considerable evidence suggests that circadianrhythmicity is progressively disrupted in senescence.

Among clock genes, Period3 (PER3) has been associated

with circadian phenotypes, homeostatic regulation of sleep,and cognitive performance in young adults. However, the

effects of PER3 genotype on aging-related changes in both

cognitive function and cortical integrity remain largelyunknown. To shed light into this issue, we have investi-

gated differences in cognitive performance, patterns of

cortical thickness, and cortical glucose consumption innormal elderly subjects homozygous carriers of the short

(PER34/4, n = 32) and long repeat alleles (PER35/5,

n = 32). Relationships between cognitive performance andcortical thickness/metabolism were further explored for

each PER3 genotype. We found that PER35/5 carriers had

poorer cognitive performance (attention, executive func-tion, semantic memory, and verbal fluency) and lower

cortical integrity (structural and functional) than PER34/4.

PER35/5 further showed thinning of temporo–parietalareas, and reductions of glucose consumption in fronto–

temporo–parietal regions bilaterally. Moreover, PER35/5

subjects exhibited significant correlations between

decreased glucose metabolism in fronto–parietal regions

and poorer cognitive flexibility, though only correlationswith lower glucose consumption of the supramarginal

gyrus distinguished PER35/5 from PER34/4 groups. Overall,

these findings enhance our understanding on the gene–

brain interaction in aging, and may have further implica-tions for the detection of subclinical cognitive decline

associated with PER3 genotypes in late life.

Keywords PER3 ! Aging ! Cortical thickness ! Corticalmetabolism ! PET ! Cognitive function

Introduction

Aging is a heterogeneous phenomenon that involves aprogressive loss of physiological reserve and deregulation

of multiple biological systems, resulting in different levels

of resilience and physical health outcomes. Dysfunctions ofthe master circadian pacemaker, i.e., the suprachiasmatic

nuclei (SCN) of the hypothalamus, are common in aging,

likely due to alterations in synaptic transmission and cel-lular physiology (Farajnia et al. 2014). Several lines of

evidence have further supported a link between circadian

dysfunctions and the severity of aging-related pathologicalconditions, such as neurodegenerative, cardiovascular,

metabolic, and mood disorders (Kondratova and Kondratov

2012).Circadian rhythms are regulated by a set of canonical

clock genes highly conserved across species, with allelicvariants influencing individual rhythms at different levels

of organization (Brown et al. 2008). In mammals, the clock

machinery of the SCN is maintained by complex tran-scriptional–translational feedback loops involving core

clock genes as period (PER1, PER2, and PER3), cryp-

tochrome (CRY1 and CRY2), CLOCK, brain and muscleARNT-like proteins 1 and 2 (BMAL1 and BMAL2), and

basic helix–loop–helix transcription factors DEC1 and

DEC2 (Reppert and Weaver 2001). A recent body of evi-dence supports that clock gene expression also occurs in

& Jose L. Canterojlcanlor@upo.es

1 Laboratory of Functional Neuroscience, Spanish Network ofExcellence for Research on Neurodegenerative Diseases(CIBERNED), Pablo de Olavide University, Ctra. de UtreraKm 1, 41013 Seville, Spain

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DOI 10.1007/s00429-017-1513-0

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the cerebral cortex (Rath et al. 2013; Li et al. 2013; Lim

et al. 2013; Cermakian et al. 2011; Yang et al. 2007). Thisfinding is of paramount importance for aging because

cortically dependent phenomena such as higher cognitive

functions (Wright et al. 2012; Reid et al. 2011) and solubleamyloid-beta levels (Huang et al. 2012; Kang et al. 2009),

one of the primary neurotoxic agents in Alzheimer’s dis-

ease pathology (Hardy and Selkoe 2012), have showncircadian fluctuations that are largely affected by age.

The PER gene family plays a key role in determining thelength of the circadian oscillation through reversible

phosphorylation and regulated degradation of PER proteins

(Chiu et al. 2008; Virshup et al. 2007). Human PER3contains a variable number of tandem repeat (VNTR)

polymorphism consisting of either 4 or 5 repeated 54-bp

sequences encoding 18 amino acids (PER34 or PER35)(Ebisawa et al. 2001). PER3 polymorphisms have been

associated with circadian phenotypes, cognitive perfor-

mance, and homeostatic regulation of sleep (Dijk andArcher 2010). Thus, previous studies have shown that

young homozygous carriers of the long repeat allele

(PER35/5) have diurnal preference, greater sleep propen-sity, and poorer cognitive performance after sleep depri-

vation than those carrying the two short repeat alleles

(PER34/4) (Maire et al. 2014; Lazar et al. 2012; Groegeret al. 2008; Viola et al. 2007).

The influence of PER3 allele status on the aging brain is

largely unknown, as revealed by the lack of research attissue or macroscopic level. Several studies performed in

peripheral blood cells have shown that the expression

pattern of PER3, but not PER1 and PER2, is altered inolder adults (Ando et al. 2010; Hida et al. 2009), likely

contributing to aging-related changes in sleep homeostasis

and circadian rhythmicity (Viola et al. 2012). As dailyrhythms of PER3 expression are ubiquitous in the human

cerebral cortex (Li et al. 2013; Lim et al. 2013) and PER35/5

carriers are more vulnerable to sleep loss throughout life,in terms of higher homeostatic sleep response and poorer

cognitive performance (Maire et al. 2014; Lazar et al.

2012; Groeger et al. 2008; Viola et al. 2007), our firstprediction is that the structural–functional integrity of the

cerebral cortex should be less preserved in elderly PER35/5

individuals, and hence, cognitive deficits should be moreevident in PER35/5 than in PER34/4 carriers. Given that

executive functions and their neural correlates in the pre-

frontal cortex are highly vulnerable to aging (Buckner2004) and that frontal areas are especially compromised in

sleep-deprived young PER35/5 carriers during executive

performance (Vandewalle et al. 2009), we further predictthat the relationship between decreased frontal integrity

and lower executive function should be stronger in older

PER35/5 than in PER34/4 groups.

To test these predictions, we first compared patterns of

cortical thickness and cortical metabolism, examined with18F-fluorodeoxyglucose positron emission tomography

(FDG-PET), between normal elderly subjects with PER34/4

and PER35/5 polymorphisms, and then evaluated whetherdifferences in cortical integrity (structural and/or func-

tional) between both homozygous genotypes were associ-

ated with variations in cognitive performance.

Materials and methods

Subjects

This study included 180 healthy older (HO) subjects who

participated in different research programs conducted inthe Laboratory of Functional Neuroscience at Pablo de

Olavide University. Participants were primarily recruited

from senior citizen’s associations, health screening pro-grams, and hospital outpatient services. Spanish was the

native and the primary language of all participants. They

showed normal cognitive performance in the neuropsy-chological tests relative to appropriate reference values for

age and education level. Individuals with medical condi-

tions and/or history of conditions that may affect brainstructure or function (e.g., stroke, coronary heart diseases,

obstructive sleep apnea, diabetes, head trauma, neurode-

generative diseases, depression, hydrocephalus, intracra-nial mass, MRI infarcts, and/or the use of psychoactive

medication) were not included in the study. All subjects

showed a global score of 0 (no dementia) in the clinicaldementia rating (CDR) as well as normal independent

function—assessed by the Spanish version of the Interview

for Deterioration in Daily Living Activities (Bohm et al.1998). Depression was excluded (scores B5) by the shorter

version of the Geriatric Depression Scale (Yesavage et al.

1983). The Ethics Committee of the Pablo de OlavideUniversity approved the study, and participants gave their

written informed consent.

PER3 genotyping

All subjects were genotyped for the PER3 VNTR poly-morphism. Genomic DNA was extracted from whole blood

using the salting-out method (Miller et al. 1988) and stored

at -80 "C until analysis. PER3 genotypes were obtainedwith an allele-specific polymerase chain reaction (PCR)

using the following primers: 50-GTGTCTTTTCA

TGTGCCCTTACT-30 (forward) and 50-TACCCCAATATACCTGACAAAAA-30 (reverse). The PCR was carried

out in a final volume of 30 lL, consisting of 150 ng of

genomic DNA, 10 pM of each primer, 0.20 mM of each

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dNTP, 1 9 Green GoTaq# Flexi buffer (Promega), 2 mM

of MgCl2, and 2.5 units of GoTaq# Hot Start Polymerase(Promega). Conditions of amplification included an initial

denaturation step at 95 "C for 2 min, followed by 40 cycles

of denaturation at 95 "C for 1 min, annealing at 58 "C for 1min, and extension at 72 "C for 1 min. The resulting PCR

products were separated by electrophoresis in 2% agarose

gel, allowing us to distinguish the 4-repeat polymorphismwith 453 bp and the 5-repeat polymorphism with 507 bp.

As part of the quality control, we confirmed that the minorallele frequency was above 0.01, and the genotype distri-

bution of the total sample (n = 180) was in Hardy–

Weinberg Equilibrium (v2 = 0.29, p = 0.58). Of screenedelderly subjects (n = 180), 56 (31.1%) were homozygous

carriers of the short repeat allele (PER34/4), 92 (51.1%)

were heterozygous carriers (PER34/5), and 32 (17.8%) werehomozygous carriers of the long repeat allele (PER35/5).

This distribution is relatively similar to the previous studies

for the European population (Lazar et al. 2012; Benedettiet al. 2008).

Results showed in the present study were obtained

comparing a subgroup of normal elderly subjects withPER34/4 and PER35/5 genotypes matched in age and sex

(n = 32 in each group) to rule out potential confounds of

these variables. Although similar analyses were performedincluding all PER3 polymorphisms, results showed that the

PER34/5 group did not differ significantly from the two

other PER3 genotypes (results not shown) in either cog-nitive function or cortical integrity (structural and/or

functional).

Cognitive assessment

A neuropsychological battery covering memory, attention,executive functioning, and language was administered to

all participants in the morning (10:00–12:00). Episodic

memory was assessed with the Spanish version of theverbal paired associates subtest (immediate and delayed

forms) from the Wechsler Memory Scale-III, and

semantic memory was evaluated with the Spanish adaptedversion of the Camel and Cactus test (Bozeat et al. 2000).

The digit span subtest from the Wechsler Adult Intelli-

gence Scale was used as an index of working memory,while attention and executive functions were evaluated

with the Spanish version of Stroop test and the Trail

Making Test (parts A and B), respectively (Pena-Casa-nova et al. 2009). Language was assessed with the

Spanish version of the Boston Naming Test (Jahn et al.

2013) and semantic fluency with the verbal fluency task(fruit and vegetable category). Finally, the global cogni-

tive status was evaluated with the Spanish version of the

Mini Mental State Examination (MMSE) (Lobo et al.1979).

MRI and FDG-PET acquisition

Structural cerebral images were acquired on a PhilipsAchieva 3T MRI scanner equipped with an eight-channel

head coil. T1-weighted magnetization-prepared rapid gra-

dient echo (MP-RAGE) cerebral images were obtained foreach participant. Acquisition parameters were empirically

optimized for gray/white contrast (repetition

time = 2300 ms, echo time = 4.5 ms, flip angle = 8",matrix dimensions = 320 9 320, 0.8 isotropic voxel, no

gap between slices, and time per acquisition = 9.1 min).

FDG-PET cerebral images were acquired on a whole-body PET-TAC Siemens Biograph 16 HiREZ scanner

(Siemens Medical Systems, Germany) with in-plane reso-

lution of 4.2 mm and point-spread function of 6 mm.Subjects fasted for at least 8 h before PET examination,

and all participants were scanned at the same time of the

day (8:00–9:00 am). Intravenous lines were placed10–15 min before tracer injection of a mean dose of

370 MBq of 2-[18F]fluoro-2-deoxy-D-glucose (FDG).

Participants stayed during 30 min in a dimly lit room withtheir eyes closed to minimize external stimuli during the

FDG uptake period. PET scans lasted approximately

30 min. A transmission scan was used for attenuationcorrection, and FDG-PET cerebral images were recon-

structed with 2.6 9 2.6 9 2 mm voxel resolution using the

standard 2D back-projection filters. MRI-based correctionof FDG-PET data for partial volume effects was performed

with the PMOD software v3.208 (PMOD Technologies

Ltd., Switzerland). The FDG-PET scanning protocol wasidentical in all participants.

Estimation of surface-based cortical thicknessand cortical metabolism

MRI data were processed using the analysis pipeline ofFreesurfer v5.3 (http://surfer.nmr.mgh.harvard.edu/) that

involves intensity normalization, registration to Talairach,

skull stripping, segmentation of white matter (WM), tes-sellation of the WM boundary, and automatic correction of

topological defects (Fischl and Dale 2000). Pial/WM

boundaries were manually corrected on a slice-by-slicebasis in each participant to enhance the reliability of cor-

tical thickness measures. Particular attention was paid to

cortical regions at the border with cerebrospinal fluid toavoid partial volume effects. Manual editing of cortical

boundaries was performed by experienced laboratory staff

blinded to PER3 genotypes and cognitive results. Corticalthickness maps were finally smoothed with non-linear

spherical wavelet-based de-noising schemes, which havedemonstrated enhanced specificity and sensitivity at

detecting local and global changes in cortical thickness

(Bernal-Rusiel et al. 2008).

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To map volumetric FDG activity on the cortical surface,

we first co-registered individual FDG-PET images to T1-weighted images using PMOD tools. Next, volumetric

partial volume-corrected FDG-PET cortical images were

sampled onto the subject’s native surface, transformed tothe Freesurfer standard surface space, and smoothed with

non-linear spherical wavelet-based de-noising schemes.

Finally, FDG activity assigned to each surface vertex wasnormalized by the FDG activity of the entire cortex using

an iterative vertex-based statistical method that excludedgroup-dependent vertices in the calculation of global

activity (Park et al. 2006).

Statistical analyses

Before applying any statistical test on cognitive variables,we tested whether they deviated from normality by

applying the Shapiro–Wilk test (Table 1). Those cognitive

tests that fulfilled this assumption were introduced in amultivariate analyses of variance (MANOVA). The

equality of error variances and variance–covariance

matrices were tested with the Levene test and Box’sM tests, respectively. Differences between PER34/4 and

PER35/5 groups for the remaining non-normally distributed

cognitive variables were assessed with the Mann–WhitneyU test. As these variables were not correlated with each

other, the Bonferroni test was used to adjust confidence

intervals. The above statistical analyses were performedwith SPSS v22 (SPSS Inc. Chicago, IL).

Differences in cortical thickness and cortical metabo-

lism between PER34/4 and PER35/5 groups were assessedby applying vertex-wise analysis of variance (ANOVA) for

each cortical hemisphere. Results were corrected for mul-

tiple comparisons using a previously validated hierarchicalstatistical model (Bernal-Rusiel et al. 2010). This model

first controls the family-wise error rate at the level of

clusters by applying random field theory over smoothedstatistical maps, and next controls the false discovery rate

(FDR) at the level of vertex over unsmoothed statistical

maps limited to significant clusters. In the present study,the FDR was controlled at the 0.05 level, meaning that on

average, only 5% of the significant vertices were falsepositives.

Vertex-wise linear regression analyses were further

performed in each homozygous PER3 group to determinewhether changes in cognitive performance were associated

with variations in cortical thickness/relative cortical

metabolism. Regression analyses, corrected for multiplecomparisons by applying the same hierarchical method,

were restricted to only those regions that showed signifi-

cant differences in cortical thickness/metabolism betweenPER34/4 and PER35/5 genotypes. If correlations for any of

the analyses reached significance in at least one of the two

groups, we then assessed group differences betweenregression slopes using the hierarchical method mentioned

above.

Results

Demographic and cognitive performance

Table 1 shows mean values in demographic and cognitivescores for PER34/4 and PER35/5 groups matched in age and

Table 1 Demographic andcognitive function in PER34/4

and PER35/5 groups matched inage and sex

Normality PER34/4 PER35/5 p

Age Yes 67.7 ± 3.8 67.6 ± 4.9 n/a

Gender (M/F) n/a 18/14 18/14 n/a

MMSE No 29.1 ± 1.2 28.4 ± 1.4 0.02

Word pairs (delayed) No 5.2 ± 2 4.6 ± 2.4 0.34

Digit span No 13 ± 3.6 12.1 ± 3.1 0.31

Camel and Cactus Yes 54.1 ± 4.1 49.7 ± 3.5 0.00002*

Stroop (words) No 105.4 ± 17.3 96.2 ± 11.8 0.01

Stroop (colors) Yes 74.3 ± 11 65.7 ± 8.7 0.001*

Stroop (word-colors) Yes 49 ± 10 40.7 ± 6.4 0.0002*

TMT-A Yes 34.9 ± 8.9 43.4 ± 10.2 0.001*

TMT-B No 103 ± 52.9 127.3 ± 54.3 0.04

Boston Naming Test No 52.6 ± 5.8 51.8 ± 4.2 0.22

Verbal fluency No 18.3 ± 3.5 15.4 ± 4.6 0.002*

Results are expressed as mean ± standard deviation of scores obtained in each group. Normalityassumption was assessed with the Shapiro–Wilk test

M males, F females. TMT Trail making test; MMSE Mini Mental State Examination, n/a not applicable

*p\ 0.05 in the MANOVA (normally distributed variables) or p\ 0.0083 in the Mann–Whitney U testwith Bonferroni correction (non-normally distributed variables)

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sex. Only those cognitive variables that were normally

distributed were introduced in the MANOVA (i.e., Cameland Cactus test, Stroop test for colors and word-colors, and

the TMT-A test). The MANOVA showed a significant

main effect of genotype (F4,59 = 6.95; p\ 0.0002; Wilk’sK = 0.68). Neither the Levene test nor the Box’s M tests

yielded significant results. Univariate analyses indicated

that elderly PER35/5 carriers showed lower scores insemantic memory as revealed by the Camel and Cactus test

(F1,62 = 21.6; p\ 0.00002), attention as derived from theStroop for colors (F1,62 = 12.4; p\ 0.001) and word-col-

ors (F1,62 = 15.7; p\ 0.0002), and executive function as

revealed by the TMT-A test (F1,62 = 12.8; p\ 0.001).Among non-normally distributed cognitive variables, only

verbal fluency scores were significantly lower in PER35/5

as compared with PER34/4 after applying the Bonferronicorrection (p\ 0.002).

Group differences in cortical thickness

Differences in cortical thickness between PER34/4 and

PER35/5 genotypes are summarized in Table 2 and Fig. 1.Compared to older PER34/4 carriers, PER35/5 exhibited

significant patterns of thinning over bilateral temporopolar

cortex (left: pcorrected\ 10-3; right: pcorrected\ 10-4), leftsuperior parietal cortex (pcorrected\ 10-3), and right lin-

gual, entorhinal, and parahippocampal gyrus (pcor-

rected\ 10-4). Although the standard deviation of theclusters located in the temporopolar cortex and left superior

parietal lobe was markedly different, likely due to the small

local changes, the Levene test did not achieve statisticalsignificance for any of these clusters.

Group differences in cortical metabolism

Table 2 and Fig. 2 depict differences in cortical metabo-lism between PER34/4 and PER35/5 groups matched in age

and sex. In comparison with elderly PER34/4 carriers,

PER35/5 showed significantly lower glucose consumptionin middle frontal (left: pcorrected\ 10-4; right: pcor-rected\ 10-4), inferior parietal (left: pcorrected\ 10-4;

right: pcorrected\ 10-5), and middle temporal regions (left:pcorrected\ 10-4; right: pcorrected\ 10-6).

Correlations between cortical thickness/metabolismand cognitive performance

Next, we sought to determine whether patterns of corticalthinning and/or cortical metabolism were correlated with

cognitive performance in each group. Analyses showed that

TMT-B scores negatively correlated with glucose metabo-lism in the left supramarginal gyrus (R2 = 0.32, pcor-rected\ 0.003) and left middle frontal regions (R2 = 0.59,

pcorrected\ 10-6) across PER35/5 carriers (Fig. 3), thisrelationship being significantly stronger in PER35/5 than in

PER34/4 elderly subjects in the left supramarginal gyrus

(pcorrected\ 0.002). No significant associations betweenother cognitive tests and structural/functional cortical

changes were detected in either PER35/5 or PER34/4 groups.

Discussion

Growing evidence suggests that the impact of disadvanta-

geous genotypes on cognition and cerebral integrity is

magnified in late life, likely due to the combined effects of

Table 2 Patterns of corticalthinning and lower corticalmetabolism in elderly PER35/5

carriers

Cortical region CS (mm2) Mean ± SD cluster (mm) % Change pcorrected

PER34/4[PER35/5 PER34/4 PER35/5

Cortical thickness (MRI)

L temporal 132 4.09 ± 0.34 3.36 ± 0.83 21 10-3

L superior parietal 280 2.61 ± 0.16 2.28 ± 0.29 14 10-3

R temporal/entorhinal 272 3.79 ± 0.29 3.14 ± 0.63 20 10-4

R lingual/entorhinal parahippocampal 1539 2.21 ± 0.13 2.01 ± 0.14 9 10-4

Cortical metabolism (FDG-PET)

L middle frontal 3281 1.3 ± 0.06 1.22 ± 0.07 6 10-4

L inferior parietal 705 1.15 ± 0.08 1.03 ± 0.1 11 10-4

L middle temporal 454 0.96 ± 0.04 0.9 ± 0.05 6 10-4

R middle frontal 2088 1.33 ± 0.08 1.25 ± 0.11 6 10-4

R inferior parietal 4176 1.19 ± 0.06 1.09 ± 0.09 9 10-5

R middle temporal 530 0.97 ± 0.04 0.89 ± 0.07 9 10-6

Mean ± SD shows the mean and standard deviation of significant clusters for each group. pcorrected: p valuecorrected with the hierarchical method (Bernal-Rusiel et al. 2010)

L left, R: right; SD: standard deviation; CS: cluster size defined as the area of change (in mm2)

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aging and genetic vulnerability on neuroanatomical and

neurochemical resources (Lindenberger et al. 2008). Inmost previous studies, target genes were related to synaptic

plasticity, neurotransmitters involved in executive func-

tioning, and propensity in developing AD (Papenberg et al.2015). However, little research has been conducted on the

contribution of circadian clock genes to inter-individual

differences in aging. Here, we provide the first evidence inhumans that PER35/5, a polymorphism of the PER3 gene

associated with alterations of sleep structure (Viola et al.

2007) and higher vulnerability to sleep loss in young adults(Maire et al. 2014; Vandewalle et al. 2009; Groeger et al.

2008; Viola et al. 2007), is also related to lower cognitive

performance and reduced structural–functional integrity ofthe cortical mantle in normal elderly subjects. PER35/5 also

showed stronger associations between reduced relative

metabolism over parietal regions and decreased cognitiveflexibility than PER34/4 carriers. Although preliminary due

to the small sample size, these findings contribute to

enhance our understanding on inter-individual variabilityin aging due to circadian genetic factors, an aspect that may

be particularly relevant in vulnerable aged population

carriers of the PER35/5 genotype.

In line with the vast literature supporting the circadian

regulation of human cognitive processes (e.g., Santhi et al.2016; Burke et al. 2015; Krishnan and Lyons 2015; Mulder

et al. 2013; Dijk et al. 1992), the previous studies have

shown that PER3 allele status modulates different aspectsof executive function in young adults (Groeger et al. 2008;

Viola et al. 2007). In our study, elderly PER35/5 carriers not

only showed decreased executive function compared toPER34/4, but also poorer semantic memory and verbal

fluency. Interestingly, we also found that these cognitive

variables were related to one another, likely indicating thatboth semantic memory and verbal fluency impairment

resulted from reduced cognitive control in older PER35/5

subjects (Berberian et al. 2016). Decreased executivefunction is expected to further influence memory, because

many encoding and retrieval processes rely on controlled

processing (Simons and Spiers 2003). However, whereasexecutive function and episodic memory are strongly

affected by aging (Buckner 2004), verbal fluency and

semantic memory are relatively preserved (Spaniol et al.2006; Allen et al. 2002). The disparity of these findings is

likely due to the influence of other genes on these cognitive

aspects (Erickson et al. 2008) and/or the impact of different

Fig. 1 Pattern of cortical thinning in elderly PER35/5 subjects. Left(L) and right (R) panels correspond to differences in the left and rightcortical hemispheres, respectively. Significant patterns of corticalthinning are displayed on inflated cortical surfaces (left superiorcorner of each panel) and on flattened cortical surfaces (right superiorcorner of each panel). Squares with colored borders limited thelocation of significant regional changes. The surface of the square waszoomed on flattened cortical maps (bottom panels) displayingcytoarchitectonic delimitation of affected regions. Abbreviations forthe left superior parietal (square with blue borders): PCL posteriorparacentral lobule, CiS cingulate sulcus, PrC precuneus, SuPSsubparietal sulcus, CPD cingulate postdorsal, POS parieto-occipital

sulcus, CS central sulcus (Scheperjans et al. 2005). Abbreviations forthe left temporal lobe (square with yellow borders): In insula, PIparainsular cortex, TA temporal area, TAr temporal area rostral, TGtemporal pole, TAp temporal area polysensory, TEd temporal areadorsal (Ding et al. 2009). Abbreviations for the right temporal lobe(square with orange borders): LG lingual gyrus, FG fusiform gyrus,PHC parahippocampal cortex, EC entorhinal cortex, iTC inferior-temporal cortex, TE temporal area, TEd temporal area dorsal, TAptemporal area polysensory, TA temporal area, TAr temporal arearostral, TG temporal pole, In insula, PI parainsular cortex (Ding et al.2009)

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experimental approaches (i.e., cross-sectional vs. longitu-dinal) on studying the influence of aging, environment, and

genes on cognitive decline (Jagust 2009).

Reduced metabolic activity in regions of the fronto–parietal network of elderly PER35/5 individuals was asso-

ciated with decreased cognitive flexibility, an important

aspect of executive function (Arbuthnott and Frank 2000).Although the capacity of the dorsolateral prefrontal cortex

to maintain executive functions has shown to be compro-

mised in young PER35/5 carriers (Vandewalle et al. 2009),the present study revealed that the supramarginal gyrus

contributes to a greater extent to account for differences

between PER34/4 and PER35/5 older adults. This is notsurprising given that the supramarginal gyrus has been

largely considered as part of a widely distributed fronto-

posterior network involved in the flexible shifting andupdating of information in working memory (Nyhus and

Barcelo 2009).

The most striking finding of the present study is thereduced structural and functional integrity of the cortical

mantle showed by older PER35/5 individuals. While PER3

polymorphisms are not associated with AD (Pereira et al.2016), our results showed that cortical regions affected in

PER35/5 match with those affected in preclinical AD

(Driscoll et al. 2012; Rowe et al. 2007; Gomez-Isla et al.1996). Mechanisms by which the PER35/5 polymorphism is

associated with the loss of cortical integrity remain elusive.

PER35/5 subjects live under a higher sleep pressurethroughout life (Maire et al. 2014) accompanied by

increased slow wave activity (SWA) after sleep loss—a

Fig. 2 Patterns of cortical hypometabolism in elderly PER35/5

subjects. Left (L) and right (R) panels correspond to differences inthe left and right cortical hemispheres, respectively. Significantpatterns of lower cortical metabolism are displayed on inflatedcortical surfaces (on the top of each panel) and on flattened corticalsurfaces (left and right superior corners of each panel). Squares withcolored borders limited the location of significant regional changes.The surface of the square was zoomed on flattened cortical mapsdisplaying cytoarchitectonic delimitation of affected regions. Abbre-viations for the middle frontal region (square with orange borders):MFG middle frontal gyrus, 8Ad dorsal part of area 8A, 8Av ventralpart of area 8A, 9 superior frontal gyrus, 9/46d dorsal part of middlefrontal gyrus, 9/46v ventral part of middle frontal gyrus, 10 frontalpole, IFs inferior frontal sulcus, PCs paracingulate sulcus, ORs orbitalsulcus (Petrides and Pandya 1999). Abbreviations for the inferiorparietal region (square with yellow borders): 1 area 1 primarysomatosensory cortex, 2 area 1 primary somatosensory cortex, 3a area

3a primary somatosensory cortex, 3b area 3b primary somatosensorycortex (Geyer et al. 1997); OP1 caudal operculum, OP4 rostraloperculum, PF area parietalis, PFop area parietalis tenui corticalisopercularis, PFt parietal rostral area of inferior parietal lobe withtenuicorticalis neurons, PFm area parietalis magnocellularis, PGarostral region in the caudal inferior parietal cortex, PGp caudal regionin the caudal inferior parietal cortex, 7A anterior region of thesuperior parietal lobe, 7PC area posterior to the post-central sulcus ofthe superior parietal lobe, POS parieto-occipital sulcus, PrC pre-cuneus, PCL paracentral lobe (Caspers et al. 2006); hIP3 intraparietalarea 3, hOc4d dorsal occipital, area 4, V1 visual area 1, V2 visual area2 (Malikovic et al. 2016). Abbreviations for the middle temporal lobe(square with blue borders): LG lingual gyrus, FG fusiform gyrus,PHC parahippocampal cortex, EC entorhinal cortex, iTC inferior-temporal cortex, TE temporal area, TEd temporal area dorsal, TAptemporal area polysensory, TA temporal area, TAr temporal arearostral, TG temporal pole (Ding et al. 2009)

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putative marker of sleep homeostasis occurring during slowwave sleep (SWS) (Borbely 1982). SWS, in turn, promotes

memory consolidation processes orchestrated by cortical

slow oscillations that determine changes in synaptic

weights underlying memory storage (Wei et al. 2016;Walker and Stickgold 2006). Both the reduced glucose

metabolism over frontal regions and the lower memory

performance observed in PER35/5 elderly individuals might

Fig. 3 Correlations between relative cortical metabolism and TMT-Bscores in elderly PER34/4 and PER35/5 carriers. a Left panel showsresults of linear regressions represented on inflated cortical surfacesbetween relative cortical metabolism and TMT-B scores in PER35/5

carriers. As illustrated in the scatterplot (right panel), only PER35/5

subjects showed significant associations between reduced relativemetabolism in left middle frontal and poorer performance in theTMT-B test, though PER34/4 individuals showed a trend towardssignificance. b Compared to older PER34/4 subjects, PER35/5 carriersshowed a significantly stronger association between reduced meta-bolism in left supramarginal gyrus and poorer performance in theTMT-B test (left panel). Note that the regression lines of each groupdepicted in the scatterplot (right panel) showed opposite directions,but only correlations in the PER35/5 group reached significance.

Results derived from vertex-wise regression analyses were correctedfor multiple comparisons (p\ 0.05) using a previously validatedhierarchical statistical model (Bernal-Rusiel et al. 2010). Whilecortical surface-based correlations were performed in a vertex-wisemanner at the whole-cortex level (left panel), scatterplots (right panel)were computed using the peak vertex of significant clusters displayedin the cortical surface maps (left panel). FDG-PET measuresdisplayed in scatterplots (a, b) correspond to metabolic rate ofcortical glucose consumption normalized to the subject’s meanuptake, and TMT-B scores were computed as the time (in seconds)required to complete the test. Variables included in the scatter plots(a, b) correspond to the standardized residuals from a linearregression analysis with age as covariate

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stem from the higher homeostatic sleep pressure experi-

enced by these subjects on a daily basis, which may turndysfunctional in late life, leading to loss of functional

integrity in frontal regions—the main source generator of

SWA (Murphy et al. 2009)—and consequently affectingSWA-dependent mechanisms of memory consolidation.

Elderly PER35/5 subjects further showed cortical thin-

ning of the entorhinal cortex and lower relative metabolismof middle temporal regions relative to their counterparts

carrying the two short repeat alleles. Both regions are ofparticular relevance for cognitive aging due to several

considerations. First, layer II neurons of the entorhinal

cortex represent a vulnerable cell type with the potential toinitiate downstream cascades leading to aging-related

functional decrements among hippocampal neurons

(Stranahan and Mattson 2010). Second, studies performedin rats and monkeys have shown that hippocampal synaptic

responses to stimulation of their afferent fibers from the

entorhinal cortex fluctuate in a circadian manner, theintegrity of this circuit being essential for time-dependent

aspects of memory acquisition (Barnes et al. 1977). Finally,

the entorhinal cortex also contributes to memory retrievalby means of reciprocal connections with the hippocampus

and frontal regions (Takehara-Nishiuchi 2014), suggesting

that selective damage of the entorhinal cortex may speedup cognitive decline in asymptomatic elderly subjects.

This study has limitations that should be addressed in

future experiments. First, we conducted a cross-sectionalstudy, which prevented determining the role of PER35/5

genotype in accelerated aging. Future longitudinal studies

should compare the rate of cognitive decline and corticalintegrity (structural and functional) between older PER34/4

and PER35/5 carriers to establish whether the PER35/5

genotype is indeed associated with vulnerable trajectoriesof aging. Second, a potential bias that could have influ-

enced our results is the time of day chosen for PET scan-

ning (8:00–9:00), since the interaction between alertnesslevel and PER3 chronotype may have an effect on cortical

metabolism. However, this may be questionable, since

patterns of cortical hypometabolism were limited to elderlyPER35/5 carriers, a genotype that has been associated with

diurnal preference in young (Archer et al. 2003) but not in

older adults (Jones et al. 2007). Finally, the small size ofthe sample employed in the present study may lead to an

increased risk of inflated effect sizes. Therefore, these

results should be considered as preliminary and replicatedin further experiments with a larger and independent

sample.

Acknowledgements This work was supported by research grantsfrom the Spanish Ministry of Economy and Competitiveness(SAF2011-25463, PSI2014-55747-R), the Regional Ministry ofInnovation, Science and Enterprise, Junta de Andalucia (P12-CTS-

2327), CIBERNED (CB06/05/1111), and the Spanish Sleep Society.The authors declare no competing financial interests.

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