Sleep,rest-activity fragmentation and structural brain changes related to the ageing process

Sleep,

rest-activity

fragmentation

and

structural

brain

changes

related

to

the

ageing

process

Marion

Baillet

and

Christina

Schmidt

Increasingevidencesuggestsanassociationbetweentypical age-relatedchangesinsleepandbrainstructure.Herewe reviewstudiesexploringtheassociationbetweenhuman histo-pathologicalandinvivoneuroimagingmarkersofbrain structureandsleep-wakeparametersinhealthyolderadults. Evidencefrombothlarge-scaleepidemiologicalstudiesand in-labquantificationofspecificsleepsignaturesarereviewedand advantagesandpitfallshighlighted.Overall,theresultspointto anassociationbetweensleep-wakedisruptionandbothlocal anddiffusechangesinbrainstructure.Theassociativestrength largelyvariesbetweenstudiesandseemstopartiallydepend onthesleeptraitunderinvestigation.Theroleofspecific sleep-wakeregulatingmechanismsonhumancognitiveandbrain fitnessandmoreparticularlytheircausalrelationshipremains tobedisentangled.

Addresses

1_{GIGA-Institute,CyclotronResearchCenter/InVivoImaging,Sleepand} ChronobiologyLab,UniversityofLie`ge,Lie`ge,Belgium

2_{PsychologyandNeuroscienceofCognitionResearchUnit,Facultyof} PsychologyandEducationalSciences,UniversityofLie`ge,Lie`ge, Belgium

Correspondingauthor:Schmidt,Christina(christina.schmidt@uliege.be)

CurrentOpinioninBehavioralSciences2019,33:8–16

ThisreviewcomesfromathemedissueonCognitionandperception –*sleepandcognition*

EditedbyMichaelCheeandPhilippePeigneux

https://doi.org/10.1016/j.cobeha.2019.11.003

2352-1546/ã2019ElsevierLtd.Allrightsreserved.

Introduction

Overthelastfew years,growinginterest isobserved in

understanding theprotective effects of sleep onhealth

andqualityoflife,especiallyinthecontextoftheageing

population.Inhumans,thegrossframeworktosleepand

wakefulness alternation is provided by the interaction

betweenasleep-dependenthomeostaticprocessandthe

circadiantimingsystemresponsibleforendogenous

mod-ulationsof sleep and wakepropensity over the24-hour

day [1]. Those regulating processes act as important

modulators for brain and behavior (e.g. [2]) and their

potential implication in cognitive and brain ageing is

increasinglyconsidered([3–5];seealso[6]for areview).

Here we review studies exploring the association

betweenhuman post-mortemand in vivomagnetic

reso-nance imaging (MRI) markers of brain structure and

sleep-wakeparameters in healthyolder adults.As

sum-marized in Table 1, brain structural measures include

quantificationofgreymatteraswellaswhitematterfrom

cross-sectional and longitudinal designs, using

whole-brainorregionsofinterestapproaches.Wewill

differen-tiate studies investigating sleep-wake parameters

collected through (1) self-reported questionnaires, (2)

polysomnography(sleep architectureand

electrophysio-logicalsignatures),and(3)actimetryrecordings(integrity

of the temporal distribution of rest-activity cycles and

estimatedsleepfragmentationin real-lifesettings).

Association

between

brain

structure

and

self-reported

sleep

Sleep complaints are increasing while getting older

including altered sleep perception, changes in sleep

architecture or increased variability in the timing and

duration of sleep [7]. Sleep questionnaires have been

developedtoprovideageneraloverviewofthesubjective

quality of sleep. While they are often used as a first

diagnostictoolinclinicalpractice,qualitativeassessments

of sleep perception donot inform about specific

sleep-regulating mechanisms and are the consequence of an

amalgamofvariousbiologicalandenvironmentalfactors,

varyingover time.However,they remain costeffective

andeasytouseallowingthereforethegenerationoflarge

andrepresentativesamplesbyadoptingepidemiological

approaches.

Cross-sectionalstudiesdetectedan associationbetween

poorself-reportedsleepqualityandreducedgreymatter

volumeincludingorbitofrontalregions[8],insula[9],and

age-related thalamic and hippocampal atrophy [10]. A

longitudinalapproach reportedthatgreymatteratrophy

of frontal, parietal and temporal, but not hippocampal

regionswaspredictivefor poorsleepqualityat3.5years

follow up [11]. Compared to sleep quality, reports on

sleepdurationhaveproducedmore mixedresults. Most

cross-sectionalstudiesfailedtoobserveasignificant

asso-ciation between self-reported sleep duration and grey

mattervolume [9,12,13]. A longitudinal study reported

that both short (<7hours) and long (>7hours) sleep

durations were associated with higher rates of cortical

Table1

Crosssectionalandlongitudinalstudiesofsleep-wakestatesandstructuralbrainimaging

Paper Design n,meanageSD Project

Brainmeasures Mainfindings

Sleepquestionnairestudies Stoffers[8] Crosssectional n=65,40.5_9.7

Netherlandsstudyof depressionandanxiety

GMvolume–VBM8,voxel wiseanalysis

EMAwasassociatedwithlow volumewithintheleftOFC.No associationwithDIS,DMS Branger[9] Crosssectional n=51,64.110.6

Multimodalimagingofearly stageAlzheimer’sdisease

GMvolume–VBM5,voxel wiseanalysis

Highnumberofnocturnal awakeningswasrelatedtolow insularvolume.Noassociationwith sleeplatency,durationandquality Liu[10] Crosssectional n=54young,21.61.6

n=94older,66.25.1 Independentsample

GM,WM,hippocampaland thalamicvolumes–VBM8

Poorsleepqualitywasassociated withage-relatedGM,hippocampal andthalamicatrophy

Sexton[11] Longitudinal3.5years n=147,53.9_15.5 Cognitionandplasticity throughthelifespan

Corticalvolume–Freesurfer, vertexwiseanalysis Hippocampalvolumes– Freesurfer

Lowvolumewithintherightsuperior frontalcortex(crosssectional)and increasedrateofatrophywithin frontal,temporal,andparietal regions(longitudinal)waspredictive forpoorsleepquality.No associationwithhippocampal volumeoratrophy

Lo[12] Longitudinal2years n=66,67.45.8

Singapore-longitudinalaging brainstudy

GM,WM,hippocampal, ventricles,inferiorand superiorfrontalgyrivolumes –Freesurfer

Shortsleepdurationwasrelatedto increasedventricularexpansion.No associationwithotherbrainvolumes andsleepquality

Lutsey[13] Crosssectional n=312,61.75 Theatherosclerosisriskin communitiesstudy

Frontal,temporal,parietal andoccipitalcortical volume.Hippocampal,and deepgreymattervolume– Freesurfer

WMHvolumeandbrain infarcts

Noassociationbetweensleep durationandMRImeasures

Spira[14] Longitudinal8years n=122,66.68

Baltimorelongitudinalstudy ofaging

Corticalthickness– Freesurfer,vertexwise analysis

Longsleepdurationwasassociated withlowcorticalthicknessinthe inferioroccipitalgyrusandsulcusof thelefthemisphere(crosssectional). Longandshortsleepdurationwere associatedwithincreasedrateof corticalthinninginfronto-temporal area(longitudinal)

Carvalho[15] Crosssectional n=1374,>50

Mayoclinicstudyofaging

Frontal,parietal,temporal andoccipitalcortical thicknessandhippocampal volume–Freesurfer

Excessivedaytimesleepinesswas associatedwithlowcortical thicknessinfrontal,parietal, temporalandoccipitalregionsand hippocampalvolume

DelBrutto[16] Crosssectional n=237,70_8 Atahualpaproject

WMHvolume,lacunar infarctsanddeep microbleeds

Poorsleepqualitywasassociated withWMHpresenceandseverity. Noassociationwithsilentlacunar infarctordeepmicrobleeds Kocevska[17] Longitudinal5years n=2,529,55.8_6.0

Rotterdamstudy

FA,MD–tractography Sleepcomplaintswererelatedto decreasedFAinthemiddle cerebellarpeduncleandmedial lemniscusovertime.Shortsleep durationwasrelatedtoincreased WMHburdenovertimeandhigh sleepefficiencywithhighWMH volumeatfollowup.Noassociation betweenWMmeasuresatbaseline andchangesinsleepcomplaints Sexton[19] Crosssectional n=448,69.2_5.1

WhitehallIIimaging substudy

FA,AD,RD-FSL,TBSS, voxelwiseanalysis WMHvolume

Poorsleepqualitywasrelatedtolow FA,highADandRDvaluesinfrontal lobe.Noassociationwithsleep duration,sleepefficiency,WMH volumeandpersistenceofpoor sleep16yearspriorbrainimaging

Table1 (Continued)

Paper Design n,meanageSD Project

Brainmeasures Mainfindings

Yaffe[21] Crosssectional n=613,45.4 Coronaryarteryrisk developmentinyoungadults

Frontal,parietal,temporal andoccipitalFA,MD WMHvolume

Shortsleepdurationwasassociated withhighMDvaluesinfrontal, parietalandtemporalregionsand highparietalWMHvolume Ramos[22] Crosssectional n=1,244,70.0_9.0

NorthernManhattanstudy

WMHvolume Longsleepdurationwasassociated withhighWMHvolumeonlyinolder adultssufferingfromdiabetes Polysomnographicstudies

Gelber[28] Crosssectional n=167,83.7(79–95) Honolulu-Asiaagingstudy

Brainautopsy GreaterSWSdurationwas associatedwithlessbrainatrophy Dube´ [29] Crosssectional n=30young,23.492.79

n=33older,60.355.71 Independentsample

Corticalthickness–CIVET, voxelwiseanalysis

HigherSWdensitywasassociated withage-relatedinsular,superior temporal,parietalandmiddlefrontal thickness.HigherSWamplitude wasassociatedwithage-related middlefrontal,medialprefrontal, andmedialposteriorthinning Mander[30] Crosssectional n=18young,20.4_2.1

n=18older,72.46.1 Independentsample

mPFCvolume–VBM8 ReducedSWAwasassociatedwith age-relatedmPFCatrophy

Varga[31] Crosssectional n=18young,18–23 n=13older,51–85 Independentsample

mPFCvolume–Freesurfer ReducedfrontalSWAwasassociatd withage-relatedmPFCatrophy

Helfrich[32] Crosssectional n=20young,20.42.0 n=32older,73.75.3 Independentsample

mPFC,dlPFC,lateral orbitofrontalcortex,thalamic andhippocampalvolumes– VBM8

Impairedslowwave-spindle couplingwasrelatedtoage-related mPFCatrophy.Noassociationwith hippocampus,thalamus,lateral OFCanddlPFC

Fogel[33] Crosssectional n=13young,24.1_3.5 n=15older,62.23.8 Independentsample

GMvolume–FSL,voxelwise analysis

Sleepspindleswererelatedtogrey mattervolumewithinthe

hippocampus,cerebellum, cingulateandparietalcortexin youngbutnotolderadults Mander[34] Crosssectional n=20young,20.4_2.0

n=31older,73.55.2 Independentsample

MD-FSL,TBSS,voxelwise analysis

Decreasedinfastspindledensity wasassociatedwithage-related increasedMDvaluesin commissuralandprojectingfiber tracts

Gaudreault[35] Crosssectional n=30young,22.9_2.7 n=31older,59.85.4 Independentsample

FA,MD,AD,RD-FSL,TBSS, voxelwiseanalysis

SleepspindleswererelatedtoWM modificationsoverfrontalregionsin youngbutnotolderadults

Actigraphicstudies Lim[37] Crosssectional n=45,33healthyolderand

12AD,89.4(85.2–93.5) Rushmemoryandaging project

Brainautopsy Highfragmentationofsleepand activestateswasrelatedtolow galanin-immunoreactiveneuronsin theintermediatenucleus.No associationwithself-reportedsleep duration,DIS,DMS

Lim[38] Crosssectional n=315older,90.46.1 Rushmemoryandaging project

Brainautopsy Highsleepfragmentationwas associatedwithsevere

arteriolosclerosisandsubcortical macroscopicinfarcts

Wang[39] Crosssectional n=17,10healthyolderand 7AD,90.45.5

Rushmemoryandaging project

Brainautopsy Lowrest-activityamplitudewas associatedwithlow VIP-immunoreactiveneuronsinthe suprachiasmaticnucleus

while an association between short sleep duration and

ventricular expansion over two years was detected in

anotherstudy[12].Besidesleep,Carvalhoetal.reported

that excessive daytime sleepiness was associated with

reducedhippocampalvolumeandcorticalthickness,

par-ticularly in temporal regions, suggesting that not only

alteredsleepbutalsoresultingdaytimesleepinessmaybe

related tobrainstructure [15].

Regarding white matter, poor sleep quality has been

associatedwiththepresenceofwhitematter

hyperinten-sities(WMH)incross-sectional andlongitudinalstudies

[16,17].WMHarecommonlyobservedduringageingand

reflectwhitemattertissue injurysuchasdemyelination,

axonallossandgliosis[18].Importantly,WMHhavebeen

related to cognitive decline, especially executive

dys-function, and incident dementia [18]. Another study

Table1 (Continued)

Paper Design n,meanageSD Project

Brainmeasures Mainfindings

Lim[40] Crosssectional n=141,82.9(median,IQR 77.4–87.4)

Rushmemoryandaging project

Volume,surfaceandareaof Desikan-Killianyatlas regions–Freesurfer

Highsleepfragmentationwas relatedtolowvolumeswithinthe lateralorbitofrontalandinferior frontalparsorbitalisandthickness withintherightinferiorfrontalpars orbitalis.Noassociationwith corticalarea,subcorticalandWM volume,self-reportedsleepduration andnocturnalawakenings Andre´ [41] Crosssectional n=30cognitively

unimpaired,73.37 n=36SCDand/orMCI, 71.5_8.2 IMPAP+study GMvolume–SPM12,voxel wiseanalysis

Highvariabilityofsleep

fragmentationduringthefirstpartof thenightwasrelatedtolowvolume withinthethalamusincognitively unimpairedolderadults.No associationwithsleep fragmentationintensity vanSomeren[42] Crosssectional n=138,69.18.5

Independentsample

Medialtemporallobeand globalcorticalatrophy– visualrattingscales

Highrest-activityfragmentationwas associatedwithmedialtemporal lobeatrophy

Baillet[43] Crosssectional n=58,76.10.5(SEM) AMImagestudy

GMvolume–VBM8,voxel wiseanalysis

FA,MD,RD,AD-FSL,TBSS, voxelwiseanalysis WMHvolume

Lowrest-activityamplitudeandhigh sleepfragmentationwererelatedto lowFA,highMDandRDvaluesin thewholeWM.Thisassociationwas partlyassociatedwithWMH.No associationwithrest-activity fragmentation,stabilityofactivity rhythms,sleepdurationandGM volume

Kocevska[44] Longitudinal5.8years n=1,201,59.37.9 Rotterdamstudy

FA,MD–tractography Shortsleepduration,highWASO andlowsleepefficiencywere relatedtoWMmodificationsin severalWMtractsuptosevenyears afterbrainimaging.Noassociation withsleeplatencyandwithWM modificationchangesovertime Oosterman[45] Crosssectional n=162,69.18.6

Independentsample

WMHvolume–visualrating scale

Lowrest-activityamplitudeand unstableactivityrhythmswere associatedwithfrontaldeepWMH. Noassociationwithperiventricular WMHandrest-activity

fragmentation Zuurbier[47] Crosssectional n=970,59.27.5

Rotterdamstudy

WMHvolume,lacunar infarctsandcerebral microbleeds

Rest-activityfragmentationwas associatedwithhighWMHvolume andcerebralmicrobleeds.No associationwithstabilityofactivity rhythms,sleepduration,WASO, self-reportedsleepqualityand lacunarinfarcts

Abbreviations:GM=greymatter,WM=whitematter,OFC=orbitofrontalcortex,mPFC=medialprefrontalcortex,dlPFC=dorsolateralprefrontal cortex,VIP=vasoactiveintestinalpolypeptideneurons,WMH=whitematterhyperintensities,VBM=voxelbasedmorphometry,FA=fractional anisotropy,MD=meandiffusivity,RD=radialdiffusivity,AD=axialdiffusivity,TBSS=tractbasedspatialstatistics;EMA=earlymorning awaken-ings,DIS=difficultyinitiatingsleep,DMS=difficultymaintainingsleep,SWS=slow-wavesleep,SWA=slow-waveactivity,WASO=wakeafter sleeponset.

observedthatpoorsleepquality,andespeciallylongsleep

latency, was associated with modifications of Diffusion

Tensor Imaging (DTI) parameters, particularly radial

diffusivity(RD),butnotwithWMHvolume.Persistence

ofpoorsleepqualitysixteenyearspriorMRIwashowever

notassociatedwithwhitemattermeasures[19].Ofnote,

ageingisrelatedtoreducedfractionalanisotropy(FA)and

increased diffusivity parameters (mean diffusivity,MD

andRD;lessprominentforaxialdiffusivity,AD)

reflect-ingimpairedfiberintegrityduetoalossofcoherenceona

preferred direction and an increase of diffusion rate.

Changes in RD are assumed to reflect myelin damage

whilechangesinAD presumablyreflectaxonaldamage

[20]. Kocevskaet al. revealed no significant association

betweenwhitemattermeasuresatbaselineandchanges

in sleep complaints over time, but observed that sleep

complaintsatbaselinewereassociatedwithdecreasedFA

values in brainstem tracts over a five-year longitudinal

design; thereby suggesting that sleep complaints may

predict future age-related white matter modifications

[17]. With respect to sleep duration, shorter durations

(<6hours)havebeenassociatedwithhighWMHvolume

in parietalareas [21] and increased WMHburden over

time [17]. Short self-reported sleepers presented also

higherMDvaluesmainlyinfrontal,parietalandtemporal

whitematterregionsand,higherprevalenceof vascular

risk factors compared to moderate sleepers [21]. Other

studiesdidnotobserveasignificantassociationbetween

self-reported sleep duration and WMH in older adults

[13,22]; note that Ramos et al. reported an association

betweenlongsleep duration(>9hours)andWMH

vol-umeonlyin olderadultssuffering fromdiabetes[22].

Association

between

brain

structure

and

electrophysiological

signatures

of

sleep

Sleeparchitectureischaracterizedbytheultradian

alter-nation between rapid eye movement (REM) and

non-REM (NREM) sleep as assessed by polysomnographic

(PSG) recordings. Standard PSG-assessed variables and

moreparticularlyfrequency-bandspecificspectralpower

exhibits stable and trait-like-interindividual differences

(e.g. [23]) which make them interesting candidates for

brain-behaviorcorrelations.Sleepspindleshavefor

exam-ple been proposed to reflect featuresof trait and

time-varyingpropertiesofneuroplasticity[24]andhavebeen

associatedwithgeneralintelligence(e.g.[25]).

Compro-mised generation and propagation of sleep oscillations

(spindlesandslowwaves)havebeenlinkedtoage-related

corticalthinning[26].

Post-mortem assessments from theHonolulu-Asia Aging

Study [27] revealed that short slow-wave sleep (SWS)

duration was associated with more global grey matter

atrophy at death [28]. In vivo MRI studies similarly

revealed that during adulthood,changes in slow waves

densityandamplitudewereassociated withage-related

corticalthinninginfrontal,temporal,parietalandinsular

regions[29].Importantly,age-relatedgreymatteratrophy

within the medial prefrontal cortex (mPFC) has been

relatedtodecreasedslow-waveactivity[30,31]andslow

wave—sleepspindlecoupling[32],bothassociatedwith

overnight sleep-dependent memory impairment. Note

howeverthatanotherstudyreported significant

associa-tionsbetweensleepspindlecharacteristicsandgrey

mat-tervolume in younger adultsonly [33].Withrespectto

white matter, Mander et al. observed that age-related

increase of MD values in several white matter tracts,

suchasthecorpuscallosum,wasassociatedwithalossof

frontalfastsleepspindles.Thiswhitemattermodification

moderated the impairment of overnight sleep

spindle-dependent motor memory consolidation [34]. Another

study failed to observe such association in a younger

agerange[35].

Association

between

brain

structure

and

rest-activity

cycles

The application of in-lab approaches allow to uncover

processesunderlyingdifferentsleepphenotypes.At

con-trast,fieldactigraphyenablesmonitoringof masked

24-hourrest-activitycyclesoverdays,weeksorevenmonths.

Difficulty however remains in the inference of

physio-logicallymeaningful sleep phenotypes.Strength of this

approach is that, unlike single-night polysomnographic

recordings, it allows a rather unique assessment of the

temporalprofilesofrest-activitypatternsandtherebythe

inferencesabout putative temporaldisruption of

sleep-wakestates.Assuch,notonlysleep,butalsothetemporal

organization of rest-activity cycles has been associated

withcognitivefunctioning(e.g. [3–5]).

Knowledgeonhistopathological changesrelatedto

rest-activity cycles in ageing mainly arises from actigraphic

studiesoftheRushMemory andAging Project[36].In

the latter, Lim et al. observed that higher rest-activity

fragmentationwasrelatedtoalowernumberof

galanin-immunoreactiveneuronswithintheintermediatenucleus

ofthehypothalamus(homologueofthesleep-promoting

ventrolateralpreopticnucleusinrodents;[37]).

Further-more, higher sleep fragmentation was associated with

more severe arteriolosclerosis and subcortical

macro-scopic infarcts at autopsy (27% and 31% higher odds

foreach1standarddeviationincreaseof sleep

fragmen-tation;[38]).Finally,Wangetal.reportedthatalower

24-hour cycle amplitude (i.e. less differentiation between

day-time activity and night-time rest) was related to a

lowernumber of vasoactiveintestinal polypeptide

neu-rons of the suprachiasmatic nucleus implicated in the

maintainance of a high-amplitude circadian output and

photicresetting[39].

Fewstudiesevaluatedtheassociationbetween

actigra-phy-derivedrest-activityparametersandgreymatterin

older adults. Highsleep fragmentation has been

and inferior frontal regions [40], and its variability

during the first part of the night with low thalamic

volume[41].Recently,vanSomerenetal.reportedthat

rest-activity fragmentation accounted for 19% of the

variance in medial temporal lobe atrophy, evaluated

withavisualratingscale[42].Incontrast,anotherstudy

did not observe any significant association between

actigraphic measures of rest-activity cycles and grey

matter volume[43].

Regarding white matter, a population-based study

reportedthatactigraphic derivedpoorsleepquality and

short sleep duration wererelated to whitematter

mod-ifications(highFAandlowMD)uptosevenyearslater

butnotwiththeevolutionofDTIparametersovertime.

Howeverasactigraphicrecordingswerenotperformedat

baseline, causality between sleep and brain structure

remains difficulttoinferatthis stage[44].Threecross

sectionalactigraphicstudiesprovidedfirstevidencethat

rest-activity disturbances observed in older adults are

related to cerebral small vessel disease manifestations.

Oosterman et al. observed that a lower 24-hour

rest-activityamplitude,especiallydecreaseindaytime

activ-ity,andunstableactivityrhythmswererelatedtohigher

deepWMHvolumesinfrontalregions[45].Inmorethan

900participantsoftheRotterdamStudy[46],rest-activity

fragmentation has been associated with WMH volume

and cerebral microbleeds but not with the number of

lacunarinfarcts[47].IntheonlystudyusingDTI,Baillet

et al. observed that low 24-hour rest-activity amplitude

andhigh sleepfragmentationwerebothassociatedwith

diffusewhitemattermodifications(lowFA,highMDand

RD) and WMH volume. Interestingly, rest-activity

amplituderemained associatedwith whitematter

prop-ertieswhenaccountingforsleepfragmentation,

suggest-ing aspecific effectof the temporaldistributionofrest

andactivityover the24-hourday[43].

Discussion

Ageing is associated with modifications of brain

struc-ture,sleep-wakeregulationandcognitionbuthowthese

factors are related to each other remains to be

deter-mined.Atthisstage,itmightappearprematuretodraw

firm conclusions regarding region-specific implications

(see also Figure 1a for a conceptual representation).

Regions of interest based approaches mainly targeted

on frontal, temporal, hippocampal, and thalamic grey

mattervolume,consideringthepredominantimplication

of those regions in brain ageing trajectory but also in

sleep-wakeregulationormorespecifically,inthe

gener-ation and/or coupling of slow oscillations and sleep

spindles.OfthirteenMRIstudiesincludingsubcortical

structures[8–13,15,32,33,40–42,43],onlyfourreported

apositiveassociationbetween sleep-wakepatternsand

hippocampal [10,15,42]orthalamicvolumes[10,41]. In

contrast, all polysomnographic studies included inthis

Figure1

(a)

(b) _{Self-reported sleep Actigraphy Polysomnography}

Brain

ageing

Trait-like sleep phenotyping

Effect size

Sample size

Representativity

CSVD manifestations Sleep perception

Sleep macro- and microstructure Rest-activity cycles

(e.g. mPFC, hypothalamus)

Sleep-wake states _{Diffuse process}

Local process

Current Opinion in Behavioral Sciences

Conceptualrepresentationof(a)resultsfromstudiesreportinganassociationbetweensleep-wakestatesandMRI-derivedbrainstructureand(b) methodologicalconsiderations.

reviewsuggestthatadecreaseinslowwavesparameters

isassociatedwithcorticalatrophy,includingthemedial

prefrontal cortex [29–31,32] suggesting a possible

neuroanatomicalsubstratetoexplain sleep-related

cog-nitive impairment. Rather consistent results were

observed regarding white matter, and especially with

actigraphicrest-activitypatternsandcerebralsmall

ves-sel disease manifestations as WMH, cerebral

micro-bleeds and related cerebrovascular pathologies

[38,43,45,47]. Actingas adiffuse process,these

man-ifestationsdisruptnotonlythenormalappearingwhite

matterbutalso connected corticalregionsthrough

sec-ondaryneurodegeneration disrupting efficient

informa-tiontransferinbrainnetworks[48].Cerebralsmallvessel

diseaseisunderlinedbyreducedcerebralbloodflowand

blood-braindysfunctionandassociatedwithvascularrisk

factors [48], suggesting that rest-activity disturbances

mayberelatedtovascularabnormalities.

Mostofthestudiesexploringtheassociationbetweensleep

andstructuralbrainchangesatolder agereliedon

ques-tionnaire-basedsubjectiveassessments ofsleep duration

andquality.Thismightatleastpartiallyexplainthe

vari-abilityinobservedresultswithrespecttobrainstructural

changesovertheageingprocess.Itmightbeassumedthat

replicabilityofstructuralbrain-behaviorassociations

criti-callyreliesuponthedefinitionandprecisionofthesleep

traitunderinvestigation(e.g.[49]).Itisthusimportantto

completepopulation-basedsampleswithin-lab

measure-mentsofmoreunmaskedsleepphenotypeswhile

control-lingforpriorsleep-wakehistory.Withinthiscontext,

trait-likeand thusreplicable characteristics havebeen attributed

forexampletoelectrophysiological-derivedsleep

charac-teristics(e.g.[50]).Thosestudies,testingforanassociation

betweenelectroencephalographic-derivedspectralpower

measures(mainlyslow-waveactivity,spindledensityand

theircoupling)andbrainstructuremostlyrevealedstrong

effectsizes([29–31,34]butsee[33,35]).Addingcomplexity

tosuchphenotypes,asforinstancetakingintoaccountthe

dynamic interaction between REM and NREM sleep

whentestingfor putativeassociations will completethe

pictureabouttheimportanceofsleeponbrainageing[51].

It wasrecently observed forexamplethatNREMand REM

sleepstagesdifferentlyaffectcognitiveoutputslinkedto

theageing process(e.g.[52])therebyre-emphasizingon

REMsleepasanimportantcontributortobrainfunction.

Finally,theroleofthecircadianclockonshapinghuman

brain ageing has been widely underestimated. This

appearssurprisingbecauseofitswidespreadimplication

and regulation of cognitive brain function [2]. Even

thoughnotassessingtheinternalclock,actigraphyallows

toinfer abouttemporalprofiles of rest-activitypatterns.

Note that most studies have focused on sleep and its

disturbanceswithoutconsideringitasapartofa24-hour

cycle.Ofseven[40,41,42,43,44,45,47]studiesusingMRI

andactigraphy,onlyfourdescribedrest-activitypatterns

[42,43,45,47]andmostof thememphasizedan

associa-tion with white matter properties. Furthermore,

post-mortem studies provide evidence that actigraphic

rest-activity patterns are associated with degeneration of

hypothalamic neurons implicated in circadian timing

and sleep-wake regulation, regionsnot easilyreachable

withconventionalMRI[37,39].Thesestudieshinttothe

importance of the temporal framework given to sleep

regulation for brain fitness at older age. Older adults

present an advanced phase of circadian rhythms (e.g.

classicalendocrinemarkerssuchasmelatoninsecretion),

leadingtoearliersleeptimesandareducedamplitudein

bothcircadian sleepand wake consolidation [53],

puta-tivelyleadingtomorefragmentedactiveandsleepstates

(i.e. intrusion of sleep into daytime activities and

frag-mentationofnight-timesleepbywakeperiodsor

noctur-nalarousals;e.g. [54]).Note that atool regrouping

cur-rentlyavailableanalysismethodswasrecentlydeveloped

inourlabinordertocatchsuchtemporalspecificitiesin

actigraphy-derivedrest-activitydata,makingthistypeof

analysisopenlyavailableforfuturestudies(Pyactigraphy,

doi:https://doi.org/10.5281/zenodo.2537921).

Assummarizedin Figure1b,bothlarge-scale

epidemio-logicalstudiesbutalsocontrolledin-labquantificationof

specific sleep phenotypes present strengths and pitfalls

suchthattheirrespectiveoutputsshouldnotbetakenas

equalbutratherusedinacomplementarymanner.Inthe

samevein,inferencesweremostlybasedoncross-sectional

correlationsbetweenbrainandsleepvariables.

Longitudi-naldesignsaredecisivetodifferentiatebetween

intraindi-vidualandinter-individualvariabilityandtherebyofferthe

possibilitytodeterminewhethersleep-wakedisturbances

haveapredictivevalueoncognitiveandbrainchanges.In

subjectivecognitivedeclinepopulation,definedas

preclin-icalstageofAD,poorsleepqualityhasforexamplebeen

reportedtoprecedemedialtemporallobeatrophy[55].

Notethatourreviewfocusesonpossiblecontributionsof

sleep-wake states on non-symptomatic age-related

changesin brainstructure. Insights frompositron

emis-sion tomography imaging studies, not included in this

review, revealed for example that poor sleep and

frag-mented rest-activity cycles are related to Ab and Tau

burden(e.g. [56];seealso [57]for areview). The latter

might partially account for the modification in brain

structure,openingnewavenuestoexploretheassociation

betweensleepandage-relatedchangesinbrainintegrity.

Conflict

of

interest

statement

Nothingdeclared.

Acknowledgements

TheresearchersarefundedbyanERCStartingGrant(GA757763)andby theFondsdelaRechercheScientifiquedeBelgique(F.R.S.-FNRS).

References

and

recommended

reading

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